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Home My WebLink AboutCMAQ Application - Chestnut Ave North Ave.pdf 2025/2026 Congestion Mitigation and Air Quality Improvement Program (CMAQ) Application COUNTY OF FRESNO coU� O i856 Y CHESTNUT AVE & NORTH AVENUE INTERSECTION IMPROVEMENTS 2of3 $2,917,874 1 $4,290,992 $87.97/1 b. 1 Applicant Information Implementing Agency: This agency must enter into a Master Agreement with Caltrans and will be financially and contractually responsible for the delivery of the project within all pertinent Federal funding requirements, including being responsible and accountable for the use and expenditure of program funds. This agency is responsible for the accuracy of the technical information provided in the application and is required to sign the application. IMPLEMENTING AGENCY'S NAME: COUNTY OF FRESNO IMPLEMENTING AGENCY'S ADDRESS: CITY: STATE: ZIPCODE: 2220 TULARE STREET, 6T" FLOOR FRESNO I CA 93721 IMPLEMENTING AGENCY'S CONTACT PERSON: CONTACT PERSON'S TITLE: Mohammad Alimi Design Engineer CONTACT PERSON'S PHONE NUMBER: CONTACT PERSON'S EMAIL ADDRESS: (559) 600-4505 malimi@fresnocountyca.gov ProjectGeneral • PROJECT NAME: To be used in all program referenced project lists Chestnut Ave & North Ave Intersection Improvements Project/Application Number: (Priority#- Max 10 per Agency) 12of3 FTIP Proposed Project Title: (Maximum 34 characters) Chestnut Ave & North Ave Intersection Improvements Project is being submitted in the following category: ❑ Transit ❑ Cleaner Fuel Technology ® Traffic Flow Improvements ❑ Bicycle/Pedestrian ® PM-2.5/10 Reduction ❑ Miscellaneous Is the project Cost-Effective? ($88/lb. or less) YES® NOD Does the project deliver PM2.5 emission reductions? YES® NOD 2 SUMMARY OF PROJECT SCOPE: Summary of the Existing Condition, Project Scope, and the Expected Benefits: Existing Condition: The intersection of Chestnut Avenue and North Avenue is a heavily congested intersection in a mostly industrially zoned, urban area of Fresno County. It is located approximately 1.25 mile to the north of State Route 99 on Chestnut Avenue and is about 1.25 mile to the east of State Route 99 on North Avenue. Golden State Boulevard, which runs parallel to the east, is about .75 mile from the intersection on both North Avenue and Chestnut Avenue. Golden State Boulevard is a heavily traveled Minor Arterial which is the former US Interstate 99. The Central Canal runs parallel to the west side of Chestnut Avenue and the Fresno Colony Canal intersects that canal and parallels the north side of North avenue to the west. The San Joaquin Valley Railroad has a branch that runs parallel to North Avenue and crosses North Avenue west of Chestnut Avenue and the Central Canal. The intersection is one half-mile north of the unincorporated community of Malaga, an underserved area where 92% of residents are Hispanic and 80% live below the poverty line. The location is within Census Tract 15, considered an Area of Persistent Poverty. The Census Tract for the project location scored in the 99th percentile on CalEnviroScreen (Attachment K), which means that the pollution in this tract is higher than 99% of other Census tracts in the state. The left-turn phasing on east-bound and west-bound North Avenue is not protected and has a large average percentage of trucks and commercial traffic. The left- turn delay typically allows for only one truck, bus, or 2-3 cars per cycle. The traffic on both SR 99 from and Golden State Boulevard to the west, as well as the railroad, add to the traffic congestion. Protect Scope: Add signalized left turn lanes to the east and west bound lanes of North Avenue; add a right turn lane to the westbound lane on North Avenue and add a right turn lane to the northbound traffic on Chestnut Avenue; change the light phasing to accommodate the new left-turn lanes. Expected Benefits: The expected benefits of the project are to reduce congestion and carbon emissions by installing protected left turn phasing at the intersection. This measure will prevent left-turning vehicles in the existing unprotected left-turn lane from having to wait for a gap in oncoming traffic, which can lead to congestion and increased emissions. Carbon emissions and congestion can be reduced by protected left turn phasing in several ways: • Reduced idling: Left-turning vehicles in a protected lane do not have to idle while waiting for a gap in oncoming traffic. This can save a significant amount of fuel and emissions, especially for heavy-duty vehicles. At this intersection, approximately 20% of the traffic is from commercial trucking; that is, one of every five vehicles is a heavy vehicle. • Reduced acceleration and deceleration: Left-turning vehicles in a protected left-turn phase do not have to accelerate and decelerate as much as vehicles in the through lane. This is because they do not have to worry about oncoming traffic or conflicting pedestrians. Reduced acceleration and deceleration can also save fuel and reduce emissions. • Reduced traffic congestion: Protected left turn lanes can help to reduce traffic congestion at intersections. Vehicles queuing in the left-turn lanes exceed the storage capacity of the lane, thereby accumulating into adjacent through-lanes, which can lead to increased emissions by obstructing through-traffic and causing delays. This also increases the possibility of rear-end crashes and congestion of the intersection. The improvements allow left-turning vehicles traveling east-bound or west-bound on Central Avenue to proceed more safely and efficiently because the left-turn movements would allow traffic to proceed through the intersection without the hazards of oncoming traffic or conflicting pedestrians, which will result in reduced emissions. 3 PROJECT PURPOSE: Describe the main purpose of the project: The purpose of the project is to decrease traffic congestion and carbon emissions that result from on- road, highway sources by reducing idling time at the intersection. The protected left turn signals will also provide increased safety at the intersection. FTIP PROJECT DESCRIPTION: (Max 156 characters) [(Location :) + (Limits) + (;) + (Improvement)] Chestnut Ave and North Avenue Intersection Improvements — Install left turn signals on North Avenue; add left turn lanes on east and west legs of North Avenue; replace ADA curbs ramps on all four corners. PROJECT LOCATION: (Include Route # or Name, Post Mile Limits/Length of Project and Project Limits) Intersection of Chestnut Ave & North Avenue near the unincorporated community of Malaga in central Fresno County. In addition to the Location Description provided, please attach a location map to the application as specified in "Attachment G" below. The location map needs to show the project boundaries in relation to the Implementing Agency's boundaries. Functional Classification: Examples of local function include arterial, expressway, major collectors, etc., as designated within local circulation plan. Provide both local classification and federal classification if different. The federal classification takes precedence. CMAQ funds may be used on local roadways. Chestnut Ave: Minor Arterial (north), Other Principal Arterial (south); North Ave: Minor Arterial (See Attachment M) 4 Project Air Pollution Reduction in kg/day: (submit calculations as attachment) 2.910 k /da Cost-Effectiveness in dollars/pound: (refer to guidelines for methodology, submit calculations as attachment) $87.97/lb. Average Daily Traffic Volume (ADT): (also, please provide source of ADT data) Chestnut Ave: 5,400 (Source: Fresno County Traffic Census 2019) North Ave: 5,100 Source: Fresno County Traffic Census 2009 Annual Auto Trips Reduced in trips/year: (if greater than zero, calculations should be included in emissions reduction sheet attachment) 0 Annual Auto Vehicle Miles Travelled (VMT) Reduced in miles/year: (if greater than zero, calculations should be included in emissions reduction sheet attachment) 0 Air Quality Screening Criteria Code(s): (refer to Appendix A for list of codes, list all applicable) [5._02 Intersection signalization projects at individual intersections Length/Width (in miles/feet) of Any New Active Transportation Facility (Class I / II / III/ IV): N/A Length/Width (in miles/feet) of New Sidewalk: Number/Type of New Crosswalks: 0 7 0 Number of New ADA Ramps: Number/Type of New Pedestrian Signals: 4 1 4 If ITS Project, Number of Signals Connected: Length of Connected Signals: 0 1 N/A Does this project have a warrant study? (submit calculations as attachment) YES, See attachment ❑ NO® Right of Way (ROW) Impacts: (Check all that apply) ❑ Agency has site control. Project is 100% within the Implementing Agency's ROW and/or is within their control at the time of this application submittal. (This includes temporary construction easements) 5 I ® Private ROW and/or utility relocations required. Project will likely require ROW in fee ownership, permanent easements, and/or temporary construction easements from private owners and/or will require utility relocations from utility companies outside that implementing agency's governmental control. The federal ROW process involving private property acquisitions and/or private utility relocations can often take 18 to 24 months after environmental document approval. The project schedule in the application for ROWneeds to reflect the necessary time to complete the federal ROWprocess. ❑ Public ROW required. Project will likely require ROW, Easements, encroachment, and/or approval involving Governmental, Environmental, or Railroad owner's property. What is the total number of months included in the project schedule to account for all ROW and/or utility impacts selected above? 124 Anticipated ROW Certification Date. Expected date project will receive ROW certification or RFA for certification will be submitted. 2030 Is this project listed on the Financial Constrained List of the 2022 RTP? 0 Project is on the constrained project list in the 2022 RTP. RTP Project ID: FRE504060 ❑ Project is NOT on the constrained project list in the 2022 RTP. If not, does the project meet the goal and objectives of the RTP policies? YES ❑ NO ❑ Optional: Please explain why the project is not on the RTP. The CMAQ Scoring Committee may take extenuating circumstances into consideration. Project would still be reduced by 5 points at minimum. N/A Please provide any other pertinent subjective information that you would like evaluators to consider when scoring your project: A study published May 16, 2023, conducted by an international team of scientists, found that protected left turn lanes reduced carbon emissions by up to 30% at intersections with mixed traffic and high left-turn volumes. A printout of this study, published in the online peer-reviewed journal Heliyon, is included as Attachment L. While safety is not a main focus of the CMAQ program, it is a strong motivator to acquire funding to make the intersection improvements. The intersection has experienced 34 collisions between January 2020 and December 2024 (Attachment 1). There were a total of 12 severe injury collisions during this period. This intersection also experienced 8 sideswipe collisions during the 5-year period with 14 broadside and 3 head-on crashes. Adding the left turn signals will 6 Project Delivery Schedule Fund Work Phase 2026127 2027128 2028129 2029130 Total CMAQ Regional Bid Funds 68% Percent share of costs—maximum 88.53% PE 356,138 356,138 ROW 246,840 246,840 Construction 2,314,897 2,917,874 Sub-total 356,138 246,840 2,314,897 2,917,874 Local Matching Funds 32% Matching fund rate—minimum 11.47% PE 167,594 167,594 ROW 116,160 116,160 Construction 283,754 283,754 Sub-total 167,594 116,160 283,754 1,373,117 Project Total PE 523,732 523,732 ROW 363,000 363,000 Construction 3,404,260 3,404,260 Grand Total 523,732 363,000 3,404,260 4,290,992 ■ Please fill out the project delivery schedule according to the planned years of implementation for your project. ■ Note that actual programming will depend on financial capacity. ■ Please note that the cost-effectiveness calculation is based on the amount of total CMAQ funding, including any local match. ■ Any non-participating costs (non-CMAQ eligible costs) of your project need to be clearly listed in the engineers estimate, on this project delivery schedule, and on the financial plan. Is the project applying as a "construction-ready project"? Points will be awarded to projects requesting construction funding only and within the first two years of the FTIP. Please attach all available environmental and ROW certifications or documentation. Projects requesting points in this category will go through a Caltrans screening process. 7 ❑ Project is requesting funds for construction only in the first year (2026/27) of the FTIP and PE/ROW documentation is attached. ❑ Project is requesting funds for construction only in the second year (2027/28) of the FTIP and PE/ROW documentation is attached. N Project does not qualify / applicant is opting out Is the project going to follow an expedited delivery schedule? YES❑ NON Please check "yes" if your project qualifies for the construction ready and/or expedited project delivery scoring criteria and you agree to the project deliver v guidelines. Is the project leveraging additional local funds? Points will be awarded to projects that leverage additional local funds (Measure C, TDA) in addition to the required local match. ❑ Project includes a 20% local match N Project includes a 30% local match ❑ Project is not leveraging additional local funds 8 Project FundingandScalability Proposed Source of Local Match Funding: Place a checkmark in the box signifying where local matching funds for this project will be coming from and specify dollar amount. Sales Tax ❑City ❑ County ❑ Other (Transportation Development Act) Sales Tax sub-total: Gas Tax ❑ Gas Tax (Subventions to Cities) ® Gas Tax (Subventions to Counties) $1 073 117 Gas Tax sub-total: $1,073,117 _J Other Local Funds ❑ City General Funds _J © Street Taxes and Developer Fees $300,000 ❑ Local Transportation Funds ❑ Other Other Local Funds sub-total: $300,000 Transit ❑ Transit Fares ❑ Other Transit(parcel/property taxes, parking revenue, etc.) ❑ Tolls (e.g., non-state-owned bridges) ❑Other(e.g., RTEP) Transit sub-total: ❑ Tolls ❑ Bridge ❑ Corridor _J ❑ Regional Transit Fares/Measures a Z ❑ Regional Sales Tax "Measure C" Local Pass Through O 0 ❑ Regional Bond Revenue w ❑ Regional Gas Tax ❑ Vehicle Registration Fees (CARB Fees, SAFE) ❑ Other Regional sub-total: Grand Total: $1,373,117 9 Is this project scalable? YES❑ NO® If yes, specify the minimum funds required: 1 $ Please provide an explanation of scalability with specific reference to budget line items on the Financial Plan (Attachment B). Example: If a project is asking for funding to pave 10 alleyways, and applicant is willing to take funding for #X (less <10) alleyways, or a partial scope of any project, this project would be considered scalable. Would your agency accept partial funding for this project? YES® NOD If yes, please explain your contingency plan to fully fund and implement the project. If full funding cannot be awarded, the County will accept partial funding for PE and/or ROW phases and apply to future cycles for construction funding. 10 Application Application Checklist and Signature Page (Required for all applications) Attachment A Financial Plan (Required for all applications) Attachment B AB 1012 Resolution (Required for all applications) Attachment C Project Estimate (Required for all applications) Attachment D Cost-Effectiveness and Emissions Reductions Calculations (Required for all applications) Attachment E RTP Documentation (Required for all applications) Attachment F Project Location Map (Required for all applications) Attachment G Preliminary Engineering and Design, Environmental, and Right-of-Way Documentation or Certification (If needed) Attachment H Collision Report Attachment I Photos of Existing Conditions (Strongly recommended for all applications) Attachment J Additional Attachments Additional attachments may be included. They should be organized in a way that allows application reviews easy identification and review of the information. All additional attachments must be scanned into one document. Please list the additional attachments: ATTACHMENT K- CALENVIROSCREEN ATTACHMENT L - RESEARCH ARTICLE ATTACHMENT M - CRS 10N53 11 Attachment A: Project Submittal Checklist and Signature Page Name of Project: Chestnut Ave & North Ave Intersection Improvements Submitted by: Mohammad Alimi, Design Engineer Agency/Organization: County of Fresno, Department pf Public Works and Planning Check All That Apply: ® Project meets CMAQ eligibility under federal guidelines. ® Sponsor will comply with California Environmental Quality Act, the National Environmental Policy Act, the Americans with Disabilities Act, AB1012 (Timely Use of Funds), Buy America, and/or any other applicable regulations. ® Project can be obligated within the identified timelines. ® Project scope will remain the same as detailed in application. ❑x Emissions calculations are attached and show positive reduction in air pollution. ❑ If needed, a warrant study is attached. ❑ If needed, PE&D, Environmental, and ROW documentation are attached. x❑ AB 1012 Resolution is attached. ® 10 hard copies of application for regional bid are attached, and an electronic copy has been provided via email or USB flash drive. © An engineer's estimate/quote of probable costs for project is attached. ❑x All required attachments are included. ❑x I understand that incomplete or late submittals will be considered for scoring at the committee's discretion, as time allows, after scoring other projects. I certify that the information contained in the application packet is accurate to the best of my knowledge and that I am authorized to submit the following project proposal for scoring and possible programming. The agency will provide the required non-federal matching funds, and deliver the project as proposed within the scope and schedule specified in the application should the project be awarded funding. Signature of full-time agency staff authorized to enter into a contract for federal funding if selected. Signed: Printed Name: Mohammad Alimi, Design Engineer Date: 03/21/2025 12 Attachment B: Financial Plan Below, please discuss the project funding strategy, clearly indicating total cost, authorization amounts and dates for all funding sources committed or anticipated to fully fund the project and any contingency plan if anticipated funding does not materialize. Any contingency plan to provide a reduced scope, should partial funding be available, would need to take into consideration air quality benefits and demonstrate it is feasible to perform the project in deliverable segments, or with reduced scope. The total project cost is estimated to be $4.3 million, with the Preliminary Engineering (PE) and Right-of-way (ROW) phases costing $887k. The project funding strategy is to use CMAQ funding for cost effectiveness and supplement the project costs and the local match with development fees, SB-1 funds, and possibly HSIP funds. If full funding cannot be awarded, the County will accept partial funding for PE and/or ROW phases and apply to future cycles for construction funding. I certify that the information contained in the financial plan is accurate to the best of my knowledge and that I am authorized to submit the following project proposal for scoring and possible programming. The agency will provide the required non-federal matching funds, and deliver the project as proposed within the scope and schedule specified in the application should the project be awarded funding. Signed: Printed Name: Mohammad Alimi, Design Engineer Date: 03/21/2025 13 23-1184 ATTACHMENT C Resolution No. 23-370 BEFORE THE BOARD OF SUPERVISORS 1 OF THE COUNTY OF FRESNO 2 STATE OF CALIFORNIA 3 4 5 A RESOLUTION OF THE BOARD OF SUPERVISORS ) OF FRESNO COUNTY REGARDING PROJECT ) 6 DELIVERY SCHEDULES FOR FEDERAL ) RESOLUTION TRANSPORTATION PROJECT SELECTION UNDER ) 7 ASSEMBLY BILL 1012 ) 8 g WHEREAS, AB 1012 was enacted into State law, in part to provide for the "timely use" of State 10 and Federal funding; and 11 WHEREAS, the County of Fresno (County) is able to apply for and receive Federal and State 12 funding under the Congestion Mitigation and Air Quality (CMAQ) Program, the Carbon Reduction 13 Program (CRP), and the Surface Transportation Block Grant(STBG) Program; and 14 WHEREAS, the County desires to ensure that its projects are delivered in a timely manner to 15 preclude the Fresno Region from losing those funds for non-delivery; and 16 WHEREAS, it is understood by the County that failure for not meeting project delivery dates for 17 any phase of a project may jeopardize Federal or State funding to the Region; and 18 WHEREAS, the County must demonstrate dedicated and available matching funds. 19 NOW THEREFORE BE IT RESOLVED that the Fresno County Board of Supervisors (Board) 20 hereby agrees to ensure that all project delivery deadlines for all project phases will be met or exceeded. 21 BE IT FURTHER RESOLVED, that failure to meet project delivery deadlines may be deemed as 22 sufficient cause for the Fresno Council of Governments Policy Board to terminate an agency's project 23 and reprogram Federal/State funds as deemed necessary. 24 BE IT FURTHER RESOLVED, that the Board hereby directs its management and engineering 25 staffs to ensure all projects are carried out in a timely manner as per the requirements of AB 1012 in 26 accordance herewith. 27 28 /// ATTACHMENT C 1 THE FOREGOING, was passed and adopted by the following vote of the Board of Supervisors of 2 the County of Fresno this 2801 day of November , 2023, to wit: 3 4 AYES: Supervisors Brandau, Magsig, Mendes, Pacheco, Quintero 5 NOES: None 6 ABSENT: None 7 ABSTAINED: None 8 9 S Qu ro, hairman of the Board 10 of rvisors of the County of Fresno 11 ATTEST: 12 Bernice E. Seidel Clerk of the Board of Supervisors 13 County of Fresno, State of California 14 15 By Deputy 16 17 18 19 20 21 22 23 24 25 26 27 28 ATTACHMENT D COUNTY OF FRESNO DEPARTMENT OF PUBLIC WORKS AND PLANNING PRELIMINARY ENGINEER'S ESTIMATE FRESNO,CA March 20,2025 PROJECT: CHESTNUT AVE&NORTH AVE INTERSECTION IMPROVEMENTS Item No. Item Description Estimated Unit Unit Price Amount ^uantity 1 Mobilization 1 LS $ 140,000 $140,000 2 Construction Funding Sign 2 EA $ 2,000 $4,000 3 Traffic Control 1 LS $ 172,500 $172,500 4 Job Site Management 1 LS $ 5,000.00 $5,000 5 Prepare&Implement SWPPP 1 LS $ 5,000 $5,000 6 Dust Control 1 LS $ 23,000 $23,000 7 Clearing and Grubbing 1 LS $ 60,000 $60,000 8 Detectable Warning Devices 48 SF $ 35.00 $1,680 9 Roadway Excavation 500 Cy $ 70.00 $35,000 10 Finishing Roadway 1 LS $ 15,000 $15,000 11 Class II Aggregate Base 500 Cy $ 100 $50,000 12 Hot Mix Asphalt 979 TON $ 115 $112,556 13 Cold Plane Asphalt 550 Sy $ 3.50 $1,925 14 Tack Coat 1 TON $ 1,500 $1,500 15 Minor Concrete Curb Ramps and Returns 4 EA $ 8,000 $32,000 16 Adjust Water Valve Box Covers to Finished Grade 1 EA $ 1,000 $1,000 17 Adjust Electrical Vaults to Finished Grade 1 LS $ 5,000 $5,000 18 Adjust Manholes to Finished Grade 1 EA $ 1,500 $1,500 20 Signage,Striping,Pavement Markings 1 LS $ 40,000 $40,000 21 Signal and Lighting System 1 LS $ 600,000.00 $600,000 31 Miscellaneous Facilities and Operations 1 LS $ 12,000.00 $12,000 22 Culvert Extension 1 LS $ 300,000.00 $inn 000 23 Railroad Crossing Improvements 1 LS $ 1,000,000.00 $1,000,000 Street Improvements Subtotal $2,618,661 Contingency(15%) $392,799 Construction Items Subtotal $3,011,460 Construction Engineering(15%) $392,799 CON SUBTOTAL $3,404,260 Preliminary Engineering(20%) $523,732 RIGHT OF WAY ACQUISTION $363,000 PE&ROW SUBTOTAL $886,732 PROJECT TOTAL: $4,290,992 ATTACHMENT E Fresno County CMAQ Emission Calculations Project Description Chestnut Ave&North Ave Intersection Improvements Inputs to Calculate Cost-Effectiveness: Total Project Cost 4,290,883 CMAQ Dollars 2,917,874 Effectiveness Period(Life): 30 yrs Days of Use/year(D): 365 days Roadway Length(Q: 1 mile(s) Congested Traffic Volume 7600 trips per day Before Speed 8 mph After Speed 20 mph See Page 26 of 2005 ARB Methodology. Emissions Factors(From Table 4,for a 5 year Service Life): Before Speed Factor(grams/mile) After Speed Factor(grams/mile) 8 mph 20 mph ROG Factor 0.67 0.3 NOx Factor 1.3 0.95 PM10 Factor 0.08 0.040 PM2.5 Factor 0.012 0.007 Calculations: Annual Project VMT = (D)x(L)x(Congested Traffic) 2,774,000 miles/year Annual Emission Reductions(ROG,Nox and PM10)in pounds/year) 0.5 x[(VMT)x(Before Speed Factor-After Speed Factor)]/454 ROG = 1130 NOx = 1069 PM10 = 122 PM2.5 = 15 Annual Emission Reductions = ROG+NOx+PM10+PM2.5 2337.1 (Ibs/yr) Once emissions reductions have been calculated,add them together and convert pounds of emissions reductions per year to kg/day: Annual Emission Reductions (Ibs/yr) Kg/Day 2.2 Ibs/kg x 365 days/yr ROG = 1.408 Thus, NOx = 1.332 Calculated Emissions Reductions = 2.910 kg/day PM10 = 0.152 PM2.5 = 0.019 Capital Recovery Factor(CRF) — (1+1)n xi where i =Discount Rate(3%)and n =Project Life(20 years) (1+i)" —1 So,the capital recovery factor=0.07 Cost-Effectiveness of CMAQ Dollars _ (CRF x CMAQ Funding)/(ROG+NOx+PM10) 87.97 Thus, Calculated Cost-Effectiveness = $87.97 (dollars/lb.) 3/14/25,12:13 PM ATTACHMENT F FresnoTrak-Edit Project MAIN MENU LOGOUT FCOG y e au 1 " M11I 1 ' Fresno CInca " of Gaveramomes� VIEW PREVIOUS VERSIONS OF THIS PROJECT UPLOAD PROJECT DOCUMENTS PROJECT ID: FRESO4060 VERSION: 2 EST TOTAL COST: $2,600,000.00 STATUS:In Progress ess-Programmed RANK SCORE: LAST MODIFIED BY: Estefany Yillafan(1/17I2O25) APPROVED BY: N/A ISH TORY f±L Egg OBLIGATION ADMINISTRATIVE EDIT-READ-ONLY CALL FOR PROJECTS FRESNO MODELING# 26-00-RTP- INTERNAL NOTES ADMINISTRATIVE NOTES AMENDMENTNOTES l PROJECT INFORMATION MPLEME NG NC MODE-GUIDELINES [EDIT SCORE) Fresno coup v Streets&Roads-Operations CAPACITY INC BIKE PED PROJECT TITLE Chestnut and North PROJECT DESCRIPTION-GUIDELINES Traffic signal improvement to improve LOS SYSTEM ROUTE SUFFIX INTERSECTION LOCAL STREET NAME CROSSSTREET CROSSSTREET DISTANCE(Mill_ Local Y v N/A Chestnut Ave North Ave N/A PROGRAMMING INFORMATION($O) PDF DRILLDOWN REPORT HISTORICAL REVENUES) (GRAPH REVENUES) EST TOTAL PROJECT COST ESTIMATED OPEN TO TRAFFIC DATE $2,600,000.00 2040 HISTORICAL COMMENTS 0 COMPLETE PROJECT O DELETE PROJECT OO CARRY OVER FROM 22-01 Add funding to new phase Add new funding source Delete fund source ' Incram funding OTHER CHANGE REASON None Project Closure-Final Report Reduce funding Revise funding between fiscal years JUSTIFICATION-LAST UPDATED:12/10/2024- Carry over; increased estimate SUMMARY OF CHANGES(AUTOMATE) Changed Project Completion Dale: -from"2025"to"2040" Changed Change Reason: -from"New Projed"to"Increase funding" No change In project funding Total project coal remains the same at 50 https://fresnotrak.fresnocog.org/edit_project.asp?show as_ftip=&update_amend=True&isReadOnly=True&project_id=FRE504060&version=2 112 3/14/25,12:13 PM ATTACHMENT F FresnoTrak-Edit Project CONTACT FCOG 3.03s EMAIL FRESNOTRAKHELP(EDECOINTERACTIVECOM https://fresnotrak.fresnocog.org/edit_project.asp?show as_ftip=&update_amend=True&isReadOnly=True&project id=FRE504060&version=2 2/2 Q .-I or ti L W N J C Q �ytl']M. 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LO 0_ CL to 0_ co M T N T T N T p�j ri >.N T pMj T N T N T M T M �•'I T N T �•'I T W tD F- F- Lp T F- T F- LD T F- T F- m F- M T F- T F- T F- M T F- T F LD F- = M L t L t L M �L t L t L t L t L t t L N m a) m a) N m w m w N m a) m w N m (1) N m w m a) m w N m w m w N m (1) U m M > M > m M > M > m M > M > m M > m M > M > M > m M > M > rn a > ATTACHMENT J PHOTOS OF EXISTING CONDITIONS CHESTNUT AVE & NORTH AVE TRAFFFIC SIGNAL IMPROVEMENTS An its~ Figure 1: Chestnut Avenue facing north. 4 IQL Figure 2: Chestnut Avenue facing south. 1 ATTACHMENT J PHOTOS OF EXISTING CONDITIONS CHESTNUT AVE & NORTH AVE TRAFFFIC SIGNAL IMPROVEMENTS •l_ate L i -1[WIWf__I Figure 3: North Avenue facing east. { T � Figure 4: North Avenue facing west. 2 ATTACHMENT J PHOTOS OF EXISTING CONDITIONS CHESTNUT AVE & NORTH AVE TRAFFFIC SIGNAL IMPROVEMENTS ` fl x Figure 3: North Avenue facing east from railraod crossing. �-.. FR4' -,�� - `• I } IL_,�'I4S � ors—?-.. - R ..<. - Figure 4: North Avenue facing west from canal toward railroad crossing. 3 uj o m Q 3 3 a° a m � c U_ N C O u (D o -C u 2 c rn N u c = d et O O O O O O O O O O O d U O, co n 10 LM M N d to I I 1 I 1 1 I 1 I u P O O O O w O o, co �o In v cn cl 1 0 0 3 V 'p C A n n n n n n n n O . J u A A A ACL O O p V c�j 67 to ryo - O W c o � (O U7 co -0 C Y tm N •r_ G7 ` C z O I Z M U L w N N N 2 0 O I� Ln CL 174 ^d gnu{'Z*4 � F C14 �an4/ jd � "�Li LUQ W W ll I z :S �! O LU U � 2 /� W ATTACHMENT K Census Tract: 6019001500 (Population: 2,407) The results for each indicator range from 0-100 and represent the percentile ranking of census tract 6019001500 relative to other census tracts. Overall CalEnviroScreen 4.0 Percentile 99 Pollution Burden Percentile 100 Population Characteristics Percentile 79 Ozone 85 Particulate Matter 2.5 96 Diesel Particulate Matter 65 Toxic Releases 95 Traffic 25 Pesticides 95 Drinking Water 100 Lead from Housing 82 Environmental Effects Cleanup Sites 98 Groundwater Threats 94 Hazardous Waste 99 Impaired Waters 0 Solid Waste 100 PopulationsSensitive Asthma 93 Low Birth Weight 34 Cardiovascular Disease 71 Socioeconomic Factors Education 95 Linguistic Isolation 70 Poverty 94 Unemployment 88 Housing Burden 14 ATTACHMENT L Heliyon 9(2023)e16260 Contents lists available at ScienceDirect d CelPress Heliyon journal hornepage: www.cell.com/heliyon Research article Emission impacts of left-turn lane on light-heavy-duty mixedy traffic in signalized intersections: Optimization and empirical analysis Jieyu Fan a, Aoyong Li b' Anugrah 11ahi", Kun Gao d' Faculty of Engineering,Computer Science and Psychology,Department of Huntan Factors,Ulnt University,Uhn,89069,Gennany b State Key Laboratory of Automotive Safety R-Energy,School of Vehicle and Mobility,Tsinghua University,Beijing,115003,China Institute for T),ansport Studies,University of Natural Resources and Life Sciences(BOKU), 1190,Vienna,Austria d Department of Architecture and Civil Engineering,Chahners University of Technology;412 96,Goteborg,Sweden ARTI C L E I N F O ABSTRACT Keywords: Reducing emissions from the transport sector is one of the crucial countermeasures for climate Traffic emission action.This study focuses on the optimization and emission analysis regarding the impacts of left- Instantaneous emission model turn lanes on the emissions of mixed traffic flow(CO,HC,and NO.)with both heavy-duty vehicles Leh-turn lane Traffic efficiency and simulation (HDV) and light-duty vehicles (LDV) at urban intersections, combining high-resolution field emission data and simulation tools. Based on high-precision field emission data collected by Portable OBEAS-3000,this study first develops instantaneous emission models for HDV and LDV under various operating conditions. Then, a tailored model is formulated to determine the optimal left-lane length for mixed traffic. Afterward, we empirically validate the model and analyze the effect of the left-turn lane (before and after optimization)on the emissions at the intersections using the established emission models and VISSIM simulations. The proposed method can reduce CO,HC,and NO,emissions crossing intersections by around 30%compared to the original scenario. The proposed method significantly reduces average traffic delays after optimization by 16.67%(North),21.09%(South), 14.61%(West),and 2.68%(East)in different entrance directions.The maximum queue lengths decrease by 79.42%,39.09%,and 37.02%in different directions.Even though HDVs account for only a minor traffic volume,they contribute the most to CO,HC,and NOX emissions at the intersection.The optimality of the proposed method is validated through an enumeration process.Overall, the method provides useful guidance and design methods for traffic designers to alleviate traffic congestion and emissions at urban in- tersections by strengthening left-turn lanes and improving traffic efficiency. 1. Introduction The transport sector takes up around a quarter of greenhouse gas(GHG)emissions globally and plays a crucial role in realizing the ultimate goal of net-zero emissions in the era of climate change[1,2].Meanwhile,transport-related air pollution contributes to a large part of global air pollution [3,4]. Although air quality standards have been improved by government and agencies such as the US Corresponding author. " Corresponding author. E-mail addresses:aoyong@tsinghua.edu.en(A.Li),glcun@chalmers.se(K.Gao). https://doi.org/10.1016/].heliyon.2023.el6260 Received 23 March 2023;Received in revised form 26 April 2023;Accepted 11 May 2023 Available online 16 May 2023 2405-8440/@ 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). ATTACHMENT L J.Fan et aL Heliyon 9(2023)e16260 Environmental Protection Agency and the World Health Organization(WHO)to guide and facilitate air quality improvements,more than 90%of the world's population lives in areas where pollutant levels are higher than WHO air quality standards [5]. Excessive emissions in terms of GHG emissions and pollutants in the transport sector have been resulting in profound negative impacts on air quality,climate,and public health that influence almost every piece of daily life[6,71.Especially,traffic congestion due to large traffic volume reduces driving speed and thus leads to significantly higher energy consumption and emissions during operations [8,91. Therefore, it is crucial to reduce emissions in the transport sector by improving infrastructure, vehicles, and the operation of the systems from different aspects.Urban transport managers and policymakers worldwide are pressing to reduce transport emissions to fulfill the national target of reducing emissions. One of the focuses of reducing transport emissions in urban areas is to reduce traffic congestion in typical bottlenecks (e.g., intersection and freeway merging areas)and corresponding traffic emissions,taking advantage of effective infrastructure and traffic management. In traffic congestion situations, internal combustion engine (ICE) vehicles emit 5-10 times more pollutants than in normal driving conditions[101.One essential component for establishing effective infrastructure and traffic management to reduce traffic emissions is the use of vehicle emission models.These models quantify the emission patterns of vehicles under different driving conditions, providing important insights for reducing emissions and improving air quality. (e.g., speed and acceleration). Vehicle emission models are essential for evaluating and optimizing the actual performances of traffic management.The necessity of vehicle emission models has motivated researchers to develop various modeling approaches based on different data sources [11]. used a portable emission test system to test and examine the on-road fuel consumption and carbon dioxide (CO2) emissions of 60 light passenger vehicles.Their results indicated that the on-road fuel consumption and emissions under the average driving patterns were 10 f 2%higher than type-approval values and were highly influenced by speeds.Their results highlighted the necessity of measuring emissions in the type approval test based on real-world driving features [12]. studied traffic emissions in the work zone using the Comprehensive Modal Emissions Model(CMEM)to generate second-by-second emissions.They reported that fuel consumption rates and emission rates of hydrocarbons(HQ,carbon monoxide(CO),nitrogen oxide(NOx),and CO2 were highly related to traffic con- ditions. Meanwhile, the emission patterns of light-duty and heavy-duty vehicles in different traffic conditions presented different principles.Meanwhile [13],developed a method for assessing the representativeness of fuel-specific vehicle-based emission factors. The method was validated based on actual emission data for 23 selected light gasoline vehicles.Results indicated that route average emission factors varied by approximately 20%for NO,or CO,and site-specific emission factors varied by 20%for NOx and 30%for CO between sites,respectively.However,fuel-based HC emission rates varied little with engine load,between routes,or between sites. This showed that estimating vehicle operational emissions was a complex process,especially in traffic flow with much complexity and randomness[14].confirmed the importance of accurate emission modeling for different vehicle types(e.g.,hybrid electric vehicles)in the Vehicle Specific Power model and improved the emission models tailored for hybrid electric vehicles.They also made significant improvements to the emissions detection tool in terms of temporal resolution, simultaneous data recording capability and data accuracy. One of the most critical infrastructure and traffic management for reducing traffic emissions is optimizing the design of in- tersections [15-17],which are the most critical bottlenecks of urban transportation systems.To name a few [161, investigated the design of multiple target signal cycle lengths to minimize vehicle delays and traffic emissions.The simulation software INTEGRATION was used to simulate traffic demand distribution, traffic demand levels,signal timing loss times,and signal cycle lengths and to es- timate intersection delays and emissions(CO2,HC,CO,and NOx) [181.pointed out that emissions of road pollutants were related to many infrastructure parameters as well as to the intensity and type of traffic.They investigated the performances and the pollutant emissions of turbo roundabouts(CO,CO2,CH4i NOx,PM2.5,and PM10),assessed by COPERT software(European emission calculation tool)[19].studied road geometries that continuously guide drivers from the entrance to the exit while eliminating weaving and queue jumping,and investigated the impact of conventional single-lane and two-lane roundabouts on traffic emissions,traffic capacity,and safety.Their results showed that the implementation of turbo-roundabouts has no benefit in terms of reducing emissions. In urban intersections,the lane functions are generally set to left-turn(or turnaround),through,and right-turn.The left-turn lane generates the most conflict points among vehicles from different directions and thus has the most significant impact on traffic effi- ciency and emissions[16,20,211.When the left-turn traffic volume reaches a certain threshold,a dedicated left-turn lane is required to reduce the impact of left-turn vehicles in the opposite road lanes to improve traffic efficiency at the intersections [221. Therefore, rationalizing the design of left-turn lanes is one of the most effective measures to improve traffic efficiency and reduce traffic emissions at intersections[15].In the relevant studies of optimizing left-turn lanes,the main design objectives are generally to reduce conflict points and improve capacity and efficiency.For instance Ref. [231,developed a method to determine the length of left-turn lanes at signalized intersections that can prevent spillover.The study considered intersection capacity,arrival rates,different signal schemes, and sequences of left-turn and through traffic to obtain the probabilities of lane blockage and lane overflow to calculate the recom- mended length of left-turn lanes.Yao and Zhang(2013)proposed three models to optimally allocate lane space and green divisions for isolated signalized intersections with short left-turn lanes.Two performance metrics were proposed to compare the performance of the three models and to investigate their sensitivity to the model parameters.Afterward,Bing et al.(2014)investigated the impact of lane configurations on traffic emissions based on the traffic simulation tools VISSIM and VSP emission models. Traffic emissions under different lane configurations were analyzed in different scenarios in terms of five indicators,including average delay per vehicle,the average number of stops per vehicle,and total emissions of CO,HC,and NO,,.Results showed that the presence of dedicated left-turn lanes (with or without widening)had a significant impact on the traffic flow and emission characteristics of the intersection [241. focused on improving the operation of urban intersections,which are often congested and a key bottleneck of the road network.The study proposes a model that integrates an improved optimal velocity model and a multi-intersection signal state function to analyze traffic flow, including vehicles turning left accurately. The model also considers pollutant emissions and has been tested through 2 ATTACHMENT L J.Fan et aL Heliyon 9(2023)e16260 simulation analysis to show its effectiveness in describing actual traffic flow [25]. proposed an optimization model for minimizing delay in traffic for left-turns at signalized intersections using exit-lanes for a left-turn(EFL)traffic organization.The model considered the relationship between the pre-signal start node of the EFL and the queuing dissipation time of left-turn vehicles.The validation of the model showed that a well-designed pre-signal control scheme can improve capacity and reduce emissions while minimizing average vehicle delays compared to conventional left-turn lanes. There is extensive literature on the effects of left-turn lanes on traffic flow characteristics (e.g.,travel time and speed) [26,27]. However,there is much less research about the impact of dedicated left-turn lane settings on different exhaust emissions.In particular, most existing studies ignored the complexity of mixed traffic flow with different vehicle types and mainly focused on single traffic flow with merely light-duty passenger vehicles.Few studies have investigated the left-turn lane optimization specific for mixed traffic flows, even though heavy-duty vehicles(HDVs)and light-duty vehicles(LDVs)have significantly distinct emission patterns,vehicular sizes, and kinetic characteristics[12-14].Therefore,the left-turn lanes at the intersections for mixed traffic are anticipated to have different design principles and should be tailored based on new methods for mixed traffic rather than existing methods for LDVs. To address the gap, this study focuses on the optimization and emission analysis regarding the impacts of left-turn lanes on the emissions(CO,HC,and NO,,)of mixed traffic flow with HDVs and LDVs,combining high-resolution field emission data and simulation tools.We utilize portable OBEAS-3000 to collect high-precision emission data for LDVs and HDVs in various traffic scenarios.Based on the field data,we first develop separate instantaneous emission models for HDVs and LDVs under various operating conditions.Then, we formulate a tailored optimization model to determine the optimal left-lane length considering the penetration rate of HDV and traffic volumes from different directions at the intersection.Finally,we empirically validate the proposed model and analyze the effect of the length of the left-turn lane (before and after optimization) on the emissions of mixed traffic flows based on the established emission model and microscopic traffic simulations using VISSIM. The remaining sections of the paper are structured as follows. Section 2 provides a description of the emission data collection process and emission modeling.In Section 3,we elaborate on the model to determine the length of the left-turn lane for mixed traffic flow.Section 4 describes the simulation method and empirical case study,followed by concluding remarks in Section 5. 2. Emission data collection and emission model establishment This study uses the portable emission monitoring device OBEAS-3000 to collect vehicle emission data regarding CO,HC,and NO.in real traffic scenarios, considering the complexity of road conditions and the contingent nature of vehicle operating conditions.The instantaneous emission rate of CO,HC,and NO.,operating in different traffic conditions for LDV and HDV are collected to establish vehicle-type specific emission models. 2.1. Emission data collection equipment The OBEAS-3000 portable emission monitor in Fig.1 was used to continuously collect the instantaneous emission of CO,HC,NO.,, and corresponding vehicle operating dynamics, including positions (coordinates), speed, and accelerations. The data acquisition frequency is 10 Hz,namely,ten times in a second.The data reflect the quantitative relationship between instantaneous emissions and vehicle dynamics in a high resolution [281.The experimental vehicles include both LDVs and HDVs. LDVs refer to Ml,M2, and Nl vehicles with a total mass not exceeding 3.5 tonnes,while HDVs refer to vehicles with a total mass exceeding 8 tonnes[291.The LDVs in this study were the Volkswagen Lavida and Harvard SUV,which were typical and popular household passenger cars in China.For HDVs,we used the vehicle of FAW Liberty.The petrol emission standards for the experimental vehicles were Chinese National IV,with engine displacements of 1.6 L(Volkswagen Lavida), 2.0 L(Harvard SUV), and 6.6 L for the HDV. The detailed parameters of the experimental LDVs and HDV are summarized in Table 1. The vehicles were driven in the urban contexts of Shanghai,China in the daytime to collect the emission data under real traffic conditions.After experiments,a total of 170972,66804 and 52251 valid records were finally collected for Volkswagen Lavida,Harvard SUV,and the heavy-duty vehicle,respectively. P Fig.1. OBEAS-3000 portable emission monitor. 3 ATTACHMENT L J.Fan et a1 Heliyon 9(2023)e16260 Table 1 Technical details of the experimental vehicles. Model/Parameters Light vehicles Heavy vehicles Brands Volkswagen Lavida Harvard SUV FAW Liberty Mass(kg) 1285 1725 15790 Engine Displacement(L) 1.6 2.0 6.6 Fuel type Petrol Petrol Diesel Emission standards State IV Standard State IV Standard State IV Standard Year of manufacture 2010 2014 2012 2.2. Instantaneous vehicle emission models for light-and heavy-duty vehicles The VSP calculation model can effectively describe the instantaneous emission characteristics of vehicles,and it has a higher time and driving state resolution than other macroscopic emission models,which can effectively express the time-varying characteristics of traffic emissions.Vehicle emission patterns depend highly on vehicle dynamics during operations,which is a complex process.In this study, we adopt the well-known Vehicle Specific Power (VSP) model to establish the relationship between vehicle dynamics and instantaneous emissions of different exhausts.Utilizing field data we have collected in Shanghai of China,we develop instantaneous emission models for both LDVs and HDVs in terms of CO, HC and NOx for quantifying vehicle emissions.VSP is the instantaneous power per unit mass of a vehicle(kW/t),and the transient emissions of a vehicle are closely related to the VSP values(30].It should be noted that the VSP models for vehicles in different countries may be different due to different vehicle emission standards.Herein,we use the field emission data in Shanghai for empirical analysis.The formula of VSP [31) can be seen in Eq. (1). 1 C„ ; VSP=vx(a+gx grade+g-Cm)+—.P m .A-v (1) where v is the instantaneous speed,m/s.a is the instantaneous acceleration,m/s2.g is the acceleration of gravity and is set to be 9.81 m/s 2.grade is the road gradient,%. CK is the rolling resistance coefficient.pQ is the air ambient density.CD is the air resistance co- efficient.Ai is the area of the vehicle cross-section,m2.in is the total vehicle mass,kg[32].provided model parameters of VSP model for LDVs based on empirical data,and the VSP value of LDVs can be expressed by Eq. (2). VSP=v x(I.Ia+g x grade+0.132)+0.000302vi (2) In this study,the effect of the slope is not considered because the experimental areas(i.e.,Shanghai,China)are plain without much variation in altitude,so grade is set to be 0 [28].The VSP formula for HDVs is not the same as that for LDVs due to the considerable distinctions in vehicular characteristics [33].Referring to Ref. [33];this study uses the following VSP calculation formula for HDVs considering vehicle weight,front-end cross-section,and other parameters regarding HDVs. VSP=v x(a+g x grade+0.09199)+0.000169v.1 (3) Based on second-by-second speed and acceleration data,the corresponding VSP is calculated and then grouped into discrete bins, which will link to the emissions of different exhausts.Please note that even though the device can record emission and vehicle dynamic data in a high resolution(0.1s),we aggerated the data into is on account of variation and monitoring accuracy to obtain more reliable results. In terms of determining the number of VSP bins, two basic rules of thumb are generally adopted: (1) the emission rates in different VSP bins should be statistically different;and(2)the resolution of bins should be high enough to avoid minor VSP bins that dominate the estimate of emissions [341.To make full use of fine-grained vehicle operating and emission data,we divide the VSP values by a step of 1 kW/t to generate the BIN partition,which can well satisfy the aforenoted two rules. (—oo,—30] VVSP C VSPW.N;_ In— 1,n),n=(—29,29],n C 7 (4) [30,+oo) in our field data, we have collected the instantaneous vehicle dynamics, including speed and accelerations, and corresponding instantaneous emissions of exhausts(CO,HC and NOx)detected by the OBEAS-3000 system.Using Eqs(2)and(3),the VSP at a certain time slot can be calculated based on speed and acceleration. To establish the relationship between VSP values and instantaneous exhaust emissions,we group the instantaneous emission rates(CO,HC and NOx)by the VSP interval(every 1 kW/t)and then calculate the instantaneous emission rates in the same VSP interval to obtain representative emission rates within each VSP interval.Especially, the processes are separately conducted for LDVs and HDVs. The final results for the instantaneous emissions within different VSP intervals for LDVs and HDVs are summarized in Table 2.These results construct a relationship between the vehicle operating con- ditions(speed and acceleration),VSP values,and the corresponding emission rates for the different exhausts,which can be utilized for the following analysis.Particularly,there are remarkable differences in the emission rates of different exhausts in the same VSP in- terval,which corroborates the necessity to develop separate emission models for HDVs and LDVs. 4 ATTACHMENT J.Fan et aL Heliyon 9(2023)e16260 Table 2 instantaneous emission data for VSP at 1 kW/t partition for LDV and HDV. VSP LDV HDV Instantaneous emissions(mg/s) Instantaneous emissions(mg/s) CO HC NOr CO HC N% -30) 4.27 0.77 0.15 111.84 13.71 18.59 [-30,-29) 4.28 0.42 0.08 68.45 10.99 14.89 [-29,-28) 4.62 0.59 0.15 92.02 11.37 12.18 [-28,-27) 2.92 0.74 0.23 87.14 11.88 14.90 [-27,-26) 2.16 0.34 0.03 101.90 13.81 17.80 [-26,-25) 7.95 0.57 0.24 116.41 15.56 21.67 [-25,-24) 5.72 0.60 0.27 103.48 9.40 14.62 [-24,-23) 4.80 0.57 0.20 151.47 14.57 11.74 [-23,-22) 2.30 0.47 0.03 150.56 14.54 22.91 (-22,-21) 3.06 0.50 0.45 96.43 12,78 18.73 [-21 ,-20) 5.05 0.72 0.26 94.08 10.62 8.31 [-20,-19) 4,49 0.51 0.10 131.80 12.39 26.63 [-19,-18) 5.50 0.57 0.19 70.33 9.79 14.96 [-18,-17) 3.06 0.62 0.16 82.79 11.78 10.88 [-17,-16) 3.78 0.55 0.15 65.97 8.61 10.20 [-16,-15) 4.25 0.62 0.36 108.66 13,05 15.14 [-15,-14) 5.59 0.51 0.18 83.90 13.32 22.83 [-14,-13) 5.21 0.65 0.07 71.29 9.00 10.73 [-13,-12) 5.19 0.72 0.13 97.78 10.85 16.90 (-12,-11) 5.54 0.54 0.07 72.69 11.87 14.59 [-11 ,-10) 4.45 0.88 0.17 78.75 9.81 17.12 [-10,-9) 5.67 0.59 0.20 68.22 10.15 14.64 [-9,-8) 5.45 0.57 0.18 72.19 10.51 12.09 [-8,-7) 4.56 0.85 0.14 86.53 10.18 15.16 [-7,-6) 5.14 0.46 0.16 63.44 11.11 15.15 [-6,-5) 4.23 0.53 0.05 65.69 11.54 13.29 [-54) 6.22 0.95 0.24 63.83 9.04 12.25 [-4,-3) 3.74 0.49 0.10 70.91 9.54 12.91 [-3,-2) 3.94 0.69 0.06 64.36 8.84 10.11 [-2,-1) 3.13 0.53 0.12 75.58 11.72 17.61 [-1 ,0) 3.31 0.59 0.08 100.49 11.19 14.50 (0,1 ) 2.24 0.42 0.02 46.95 7.29 7.44 [1 .2) 3.56 0.65 0.07 66.88 9.38 11.09 [2,3) 4.09 0.60 0.16 60.13 9.35 13.09 [3,4) 4.67 0.71 0.09 69.81 10.73 14.52 [4,5) 7.24 0.80 0.22 103.63 9.79 15.63 [5,6) 3.90 0.56 0.14 73.96 11.21 16.24 [6,7) 6.92 0.81 0.21 87.71 9.40 11.86 [7,8) 7.82 0.84 0.10 97.53 10.64 13.26 [8,9) 5.62 0.69 0.22 91.18 10.45 14.83 [9,10) 8.96 0.82 0.41 73.50 10.37 16.70 [10'11 7.27 0.67 0.16 84.97 10.50 13,36 [11 ,12) 7.68 0.75 0.30 100.07 11.69 15.73 [12,13) 6.60 0.77 0.18 81.80 11.77 16.48 [13,14) 9.29 0.85 0.38 92.66 11.56 14.74 [14,15) 8.99 0.89 0.23 103.44 11.97 18.88 [15,16) 7.95 0.72 0.14 100.01 12.36 17.27 [16,17) 8.22 0.95 0.18 82.90 12.08 16.32 (17,18) 6.14 1.19 0.26 107.68 14.08 20.35 [18'19) 6.66 0.86 0.23 159.31 14.13 12.92 [19,20) 7.74 0.80 0.32 93.99 11.32 20.34 [20,21) 11.97 0.94 0.32 94.69 11.43 14.51 [21 ,22) 7.01 0.79 0.22 102.21 11.87 21.39 (22,23) 8.85 0.86 0.28 86.06 12.50 19.13 [23,24) 8.89 1.05 0.26 76.94 11.99 16.88 [24,25) 12.19 0.93 0.23 72.89 11.73 16.74 [25,26) 5.82 1.00 0.26 96.07 12.32 19.84 [26,27) 6.91 0.81 0.20 90.50 10.95 17.97 [27,28) 11.08 1.01 0.33 111.83 13.61 18.04 (28,29) 5.73 1.82 0.55 102.11 12.49 16.47 (29,30) 16.03 0.97 0.32 117.23 11.97 22.59 [30,+w 6.77 1.51 0.27 110.06 13.62 23.34 5 ATTACHMENT L J.Fan et aL Heliyon 9(2023)e16260 3. Determining the left-turn lane for mixed traffic flow The left-turn lane at an intersection generally includes a deceleration section(traverse section)and a storage section(see 2(a)). Vehicles complete the traverse from through to left-turn in the deceleration section and then turn left or queue to wait for the next green-light phase.Inadequate lengths of the storage section will result in two consequences,as shown in Fib;. 2(b)and(c).A large number of left-turn vehicles will cause the left-turn queue to block the adjacent straight lane,making it impassable for the through traffic,as shown in Fig.2(b).A long queue of through traffic will block the left-turn lane,preventing left-turn vehicles from passing into the storage section,as shown in 1•ig. 2(c).Short left-turn lane length can cause increased conflict between left-turning and straight- Reduced speed section Left-tum Late storage section (a) Composition of the left-turn lane 63 Left-turning vehicles Through traffic vehicles (b)Left-turning queuing vehicles obstructing through traffic Lefl-turning vehicles Through traffic vehicles � �� I -I m -4 MWO (c) Queuing of through traffic obstructing left-turning traffic Fig.2. Diagram showing the impact of insufficient left-turn lane storage length on traffic flow. 6 ATTACHMENT L J.Fan et aL Heliyon 9(2023)e16260 through vehicles,so the length of the left-turn storage section is,therefore,crucial to the operational efficiency of the intersection.Our design aim is to determine the optimal storage length of the left-turn lane for mixed traffic with different penetration rates of HDVs for reducing conflict points,traffic delays,and thus traffic emissions at the intersection.Due to the large difference in the length of HDVs and LDVs,the left-turn lane storage length for mixed traffic flow must consider not only the number of vehicles in the traffic flow but also the proportion of HDVs in the traffic flow.It is worth noting that it is implausible to directly formulate an optimization in which left-turn lane length is the decision variable and the overall traffic emission at the intersection is the objective function[15,26,27].The reason is that the quantitative relationships between left-turn lane length and traffic emission cannot be mathematically modeled directly or indirectly.However,leveraging the validated relations between traffic delays and emissions,it is plausible to formulate an optimization model minimizing traffic delays and minimizing traffic emissions indirectly.This strategy has been adopted and validated by several relevant studies [15,16,20,26,27] and is utilized herein. Assuming that the traffic flow on the road section consists of n different types of vehicles and the length of the vehicle type 1 is Li with Lt <Lz < ... <Li <••• <L,,.The proportion of vehicle type i is Pi and Pl+Pz+--- +Pi+•-- +P =1.Because the combination of two adjacent vehicles in the traffic flow is random,the probability of the combination that the preceding vehicle is the type i and the following vehicle is the type j,is P;Pj.Then,it is easy to verify,as shown in Eq. (5). PiP)-(PI +P2+...+A)2=I (5) = j=1 Assume tj is the time headway between vehicle type i and vehicle type j when the traffic volume reaches the capacities of a lane.We can estimate the average time headway in the mixed traffic of various vehicle types,as shown in Eq. (6). H,= Y_ Y_P;Pjtij (6) i=1 j=1 Based on the average time headway,the theoretical capacity of one lane for mixed traffic is 3600 3600 CP- - = i,j= 1,2,3, it (7) Y-PPjtij —1 J_t The above equations are genera for mixed traffic flows with several vehicle types.However,this study mainly investigates the case of two types of vehicles on account of the available emission models,namely n =2.Let us assume the proportion of LDVs and HDVs are p and 1-p.We use subscripts 1 and h to denote light-and heavy-duty vehicles,respectively.It can be deduced that the capacity of a lane for mixed traffic flow is 3600 3600 (8)CP= 2 41P'+(t/h+q,l)P(I-P)+thl(I -P)2 EPiP.itii i=1 j=1 In a mixed traffic flow,the arrival rate of left-turn vehicles at the intersection is 2(veh/h),and the maximum number of vehicles per hour that can pass the intersection at the left-turn green light phase is 1t.2 is set to be less than 1t.Otherwise,it will be undissipated traffic congestion. We assume that vehicle arrivals follow a Poisson distribution and time headway follows a negative exponential distribution,as most traffic flow studies did[8,9].In this regard,this is a typical WWI queuing model.As per the queueing theory, the probability that there is a queue of n,LDVs waiting in the left-turn lane at a given time is P",=P\I (9) The probability of having nh HDVs waiting in the left-turn lane is P,.h (I-P)C1 (10) The probability of fewer than N vehicles queuing in the left-turn lane is N-I N_ hi(I -P(x<N)) 1 (12) bt(a) In the N vehicles,the number of LDVs queuing in the left-turn lane N,is 7 ATTACHMENT L J.Fan et U Heliyon 9(2023)e16260 In(1 —P(x<N)) In\� v and the number of HDVs queuing in the left-turn lane Nn is N1,=(1—p) x / —1 (14) 111\A� N The maximum number of vehicles per hour that can pass the intersection at the left-turn green light phase It. _SI.GL C (15) P where SL is the hourly maximum traffic throughput of a single left-turn lane(veh/h),namely the capacity,which can be calibrated based on Eq.(8).GL is the effective green time of the left-turn protection phase(in seconds),and C is the signal cycle length in seconds. Then,we can get Eq. (l6). ,1 AC (16) R SI,GI, In mixed traffic flows,the average length occupied by an LDV while parking is approximately 1.5 times its length,with a default value of 7.6 m.Referring to relevant literature[351,the average length occupied by an HDV Ln is related to the percentage of HDV in mixed traffic and can be approximated by Eq. (17). Ll,=7.6(1+M)=7.6(2—p) (17) To accommodate the N vehicles with N,LDVs and Nn HDVs,the length of the stored section of the left-turn lane should be followed Eq. (18). 1,v=7.6N1+LhNh (18) Combining Eqs.(13),(14)and(18),the length of the stored section of the left-turn lane that wants to ensure no spillover in!'i;.2(a) at the probability of P is l l Ls—7.6 [ In(1 —P) —1J +7.61 (2— )(1— r ln(1 —P) 19 ( ) —1— P[In(AC)—In(SI.GL) I P)[In(AC)—In(SI.GI.) The value of P denotes the probability of ensuring no spillover in the left-turn storage lane and is the empirical value that considers the tradeoff between construction costs and service levels.If the value of P is too large,the length of the left-turn storage lane will be very long,which can ensure service levels but be a waste of the lane in most periods.If the value of P is too small,there will be a high risk or probability of left-turn lane spillover.Based on the arrival rate of left-turn traffic,the signal phases and cycles in an intersection, lane capacity of left turning,and penetration rate of HDVs in the traffic flow,we can design the corresponding left-turn lane length as per Eq. 09),which is tailored for mixed traffic. f •, � f 1 1 4 .+r�'ti SAE. Fig.3. Cao'an road—Jiasong north road intersection. 8 ATTACHMENT L J.Fan et aL Heliyon 9(2023)e16260 4. Empirical analyses based on field scenarios To validate the effects of the proposed method on the traffic emissions of mixed traffic at intersections,we conduct an empirical analysis regarding a typical intersection with mixed traffic in Shanghai,China.We use the VISSIM simulator to simulate the traffic and obtain high-resolution vehicle trajectories before and after optimizing the left-turn lane.In the simulation,we consider motor vehicles including LDVs and HDVs,but do not consider micro-mobility such as bicycles and scooters,which are out of the scope of this study. The intersection of Cao'an Road and North Jiasong Road(see Pig. 3)was chosen for the case study. The reasons for selecting the intersection are 1)the intersection has a high traffic flow with congestion in the morning and especially has high left-turn traffic flow; 2)all inlet lanes from four directions of the intersection have left-turn lanes;3)the traffic flow at the intersection has many HDVs and is a typical mixed traffic flow.At this intersection,Cao'an Road is a two-way 12-lane urban road,with each inlet lane comprising two left- turn-only lanes, three through lanes,and one right-turn lane.North Jiasong Road is a six-lane urban road in both directions,which includes one left-turn lane,one straight-through lane,and one right-turn lane.A bird's view of the intersection is shown in Fig. 3.To reflect the real traffic flow characteristics in the intersection appropriately,we have conducted field surveys in the morning peak hours at the intersection for one week(from Monday to Friday, 7:30 a.m.-8:30 a.m.).Drones were used to record video above the inter- section.Details about traffic flow in terms of traffic volumes and penetration rate of HDVs were extracted manually from the recorded videos.We did not apply computer vision techniques as manual counting is more accurate even though time-consuming.The details of the traffic flow are summarized in Table 3.Please note that we average the data from five consecutive days to get the representative values. The traffic volumes in VISSIM are inputted according to the traffic volumes at the intersections of the field survey,and the road network is built according to the actual construction of the intersections in the original scenario of the simulation.Based on the field research,the VISSIM simulation platform is built with the intersections taken in the field as the base map(see Fig. 4). The total number of LDVs in the morning peak hour was 4062, representing 85.91%of the total traffic,while HDVs were 666, representing 14.09%of the total traffic. For different directions, the proportion of LDVs ranged from 76.43%to 95.18°/a,while the proportion of HDVs ranged from 4.82%to 23.57%. It can be observed that the penetration rate of HDVs in different directions is distinct.Meanwhile,the traffic volumes from and to different directions have considerable variation as well.As indicated by the results of the field survey, there are considerable delays in different import lanes. We compare the traffic emissions and efficiency at the intersections in original and optimized scenarios.In the original scenario,we use the real and current settings about the length of left- turn lanes in different directions at the intersections.In the optimized scenario,we use our proposed method to determine the length of left-turn lanes in different directions according to the traffic volume,the penetration rate of HDVs,and signal.timing.The lengths of left-turn lanes in different directions in two scenarios are summarized in Table 4.We simulate the case of morning peak hours as per our field survey data.In the VISSIM simulation,the velocity and acceleration of each vehicle are recorded and outputted at the fre- quency of 1 s.We record the vehicles'information during the period from the time frame when they arrive 200 in away from the stop line of the entrance lane to the time frame when they leave 200 in away from the stop line of the exit lane.Based on these data,we use the developed instantaneous emission models for LDV and HDV in Section 2.2 to estimate the emissions of CO,HC,and NO.of each vehicle crossing the intersections.In this manner, the emissions of all vehicles crossing the intersection in the two scenarios can be directly quantified and compared.We repeat the simulation five times to eliminate the potential biases due to randomness.The mean values of emissions in the five simulations are used for representatives. The results of the emissions in the two comparative scenarios are summarized in'fable 4.The results show that the CO,HC,and NO, emissions of all traffic,including LDVs and HDVs at the intersections,are substantially reduced at the intersection in the optimized scenario. More specifically, the CO emission in the optimized scenario decreases by 34.43%as compared to the original scenario, which is a significant improvement.The same goes for HC and NO.emissions,which reduce by 29.77%and 30.42%in the optimized scenario, respectively. The results indicate that the proposed method for improving left-turn lane settings can cut down traffic emissions considerably in terms of CO,HC,and NO, More importantly,the emission patterns of LDV and HDV show divergences.It can be observed in Table 5 that the percentage of HDV in the mixed traffic flow is much smaller than that of LDV.The proportion of LDV in the traffic flow from different directions Table 3 Morning peak-hour Traffic volume at the investigated intersection. Entrance Turning LDV HDV Percentage of LDV Percentage of HDV Average delay(s) Max.delay(s) Max.Queue length(m) West Entrance Lek 45 21 95.18% 4.82% 81.5 197.8 103.2 Right turn 59 7 89.39% 10.61% 11.2 29.1 0 Straight 737 137 84.32% 15.68% 72.6 174.2 106.2 East Entrance Lek 313 31 90.99% 9.01% 62.9 190.1 57.6 Right turn 231 61 79.11% 20.89% 1.5 7A 0 Straight 563 72 88.66% 11.34% 83.7 183.5 154.5 North Entrance Lek 253 45 84.900/6 15.10% 57.7 146.5 124.4 Right turn 160 27 85.56% 14.44% 3.5 16.9 13.2 Straight 597 85 87.54% 12.46% 66.7 164.0 53.9 South Entrance Lek 207 58 78.11% 21.89% 88.9 191.1 169.2 Right turn 120 37 76.43% 23.57% 28.5 83.0 90.2 Straight 407 85 82.72% 17.28% 75.3 186.8 172.7 Total number vehicles 4062 666 85.91% 14.09% 9 ATTACHMENT L J.Fan et aL Hefiyon 9(2023)e16260 S? ` I_ 0 r Fig.4. Simulation scenario in VISSIM. Table 4 Storage length of the left-turn lane. Directions Length of the left-turn lane in original scenario(m) Length of the left-turn lane in optimized scenario(m) West Entrance t 70 120 East Entrance 70 93 North Entrance 50 122 South Entrance 50 63 varies from 76.43%to 95.18%,with an average value of 85.91%.The percent of HDV from different directions varies from 4.82%to 23.57%, with a mean of 14.09%. However, the emission results in 'fable 4 show that in the original scenario, the LDVs merely contribute 27.86%of CO,31.61%of HC,and 5.67%of NO, which is not proportional to the percentage of LDV in traffic volumes.In contrast,HDVs produce 72.14%of CO,68.39%of HC,and 94.33%of NO,,in the studied intersection,even though they merely take up 14.09%of traffic flows.The same phenomenon is observed in the optimized scenario.HDVs contribute the most emissions of CO,HC, and NO,,although the percentage of HDVs is not large.These results imply the necessity of specific measures for reducing the traffic emissions of traffic flow with only LDVs,and mixed traffic flow with HDVs,as well as the merits of the proposed method for mixed traffic. For LDV, the CO emissions reduce by 33.74% in the optimized scenario as compared to the original scenario. A similar reduction in CO emissions(34.7%)for HDVs is also found.The HC emissions of LDVs decrease by 17.78%,but the reduction in the HC emissions for HDVs is more notable(35.31%).The NOX emissions for LDVs reduce by 27.93%in the optimized scenario,similar to the reduction of NOX emissions for HDVs(30.57%).The results demonstrate that the optimization of the left-turn lane has similar impacts on the reduction of CO and NOX emissions for LDVs and HDVs,but has a more remarkable influence on the HC emissions for HDVs compared to LDVs. To further validate the reliability of the proposed method to determine the storage length of left-turn lanes properly, we have enumerated the relationship between the storage length of the left-turn lane and corresponding traffic emission in the north import directions at an increment of 25 m.The results are summarized in Table 5.It can be seen that the emissions of CO,HC,and NO,,from LDVs decrease with the increase of left-turn lane length firstly,reach a swale at the length of 125 m,and then increases with a longer left-turn lane.The same pattern can be observed for HDV with differences in absolute values.Interestingly,the emissions,to some extent,increase with a longer left turn,which seems counterintuitive but rational.The reason found from observing the trajectories of vehicles is that straight-through traffic would use the left-turn lane to take over preceding vehicles and cut into the straight-through lanes when the left-turn lane is rather long,as demonstrated in Fig.5.These behaviors will increase the conflicting points in the traffic and result in traffic oscillations (deceleration and acceleration behavior)due to cut-in behaviors,which will lead to higher vehicle emissions.More importantly,the best length for the left-turn lane is around 125 m in the enumeration,which is highly aligned with the theoretically derived value of 122 m based on our proposed method in Taol. o. These corroborate the validity and ability of the proposed method to determine the optimized left-turn storage length for mixed traffic properly. The setting of the left-turn lane storage not only affects emissions but also traffic efficiency at the intersection in terms of travel time and delays. Therefore,we compare the changes in the traffic efficiency before and after optimizing the left-turn lane in terms of average travel delay,maximum delay,and maximum queue length in different directions.The results are summarized in T,i hle 7.The average traffic delays in different entrance directions after optimization reduces by 14.61%(West),2.68%(East),16.67%(North),and 21.09%(South).The maximum delays of vehicles from different directions reduce by 4.73%-9.05%.The maximum queue lengths in different directions decrease significantly by 37.02%,79.42%,and 39.09%.The improvements are mainly attributed to avoiding the 10 J.Fan e[al. He(iyon 9(2023)e16260 0 u 7 'LI 6! w C o 0 0 0 o OD o JR M ll N o� y v- O, O M !� N v- In 0 fL M N Cl) M N M Cl) Cl) v u cu ar w m y O u w v u > cc d 0J d 0J O O O 0 0 G O O a ° o � o a o E m in , d� Lna, N v v .-' 0 00 0o rn r. v F N Cn�1 LO n 'cq ND N 'J P cu OJ :d O 00 C 0 Q „ a v E O0 N wU N U v- 0 0 N 0 6 N'0 0 cliV NO O O6 n v OJ J N O O O O N 1N 'D A. O O N N V* � '-+ ti ti N O Z w > = m C) Q c o � w H ° H Q v c > x C 0) W C o= O 'O O O [ O Oy 0. ° o o d o o OJ 'D N N C'l OJ G. C'M C'M CV CO -Ir P cc C O U N � U A °^ cu Q G y O v v O lu N -qr 00 v N O N coOJ O M LO LO n N n N LO y 'D r� V- N H O\ CO LO N > O O N co co r� .-' - N co C w w w w v w n d O V O O V O O V O 0 y V x Z V x .Z V x Z C O v o 0 0 o v LO a-, cu o > > F _ > m 0 ¢ S O F V z 11 ATTACHMENT L J.Fart et al. Heliyon 9(2023)e16260 Left-tuniina vehicles T-1 Through traffic vehicles Fig.5. Through traffic uses left-tum storage lane to take over. Table 6 The effects of left-turn lane storage length on traffic emission in the north import. LDV HDV Length of left-turn lane CO(g/h) HC(g/h) N%(g/h) CO(g/h) HC(g/h) N%(g/h) Original scenario(50 m) 1802.9 293.3 48.7 4426.4 601.5 767.7 75 m 1514.0 297.8 42.2 3863.1 523.6 702.1 100 m 1437.6 286.7 41.0 3794.3 513.3 695.6 125 m 1194.5 241.1 35.1 2890.5 389.1 533.0 150 m 1536.8 305.2 44.9 4125.5 553.5 757.0 175 m 1486.9 296.9 43.4 3714.8 496.5 687.4 200 m 1539.1 306.6 45.3 3683.3 492.2 681.7 Note:We use the north import direction as the representative and repeating the validation process in other directions can find similar conclusions, which are not elaborated in case of redundancy. Table 7 Comparison of emissions under emission optimization and delay optimization. West import East Import North Import South Import ORS-Average delay(s) 69.16 59.19 54.27 71.2 OPS-Average delay(s) 59.05 57.6 45.23 56.19 Reduction percentage 14.61% 2.68% 16.67% 21.09% ORS-Maximum delay(s) 197.8 190.1 164 186.8 OPS-Maximum delay(s) 181.9 181.1 155.9 169.9 Reduction percentage 8.04% 4.73% 4.94% 9.05% ORS-Maximum queue length(m) 106.2 154.5 124.4 172.7 OPS-Maximum queue length(ni) 57.8 973 25.6 105.2 Reduction percentage 45.57% 37.02% 79.42% 39.09% Note:ORS and OPS denote the original scenario and optimized scenario,respectively. harmful scenarios demonstrated in Pig.2 by providing appropriate storage lanes for left-turn vehicles.The results demonstrate that the proposed method can reduce travel delays and queues considerably and generate benefits in traffic efficiency.Although heavy vehicles only account for 13-18%of the vehicle count in the intersection, they generate more than 70%of CO emissions,over 65%of HC emissions,and more than 90%of NOx emissions from all heavy vehicles. 5. Conclusions This study investigates the optimization and emission analysis regarding the effects of left-turn lanes on the emissions(CO,HC and NOx)of mixed traffic flow with both LDVs and HDVs at urban intersections.High-resolution field emission and vehicle operating data of LDVs and HDVs in real urban contexts are collected and used to establish instantaneous emission models for HDVs and LDVs regarding CO, HC and NO, Meanwhile, a tailored model is formulated to determine the optimal left-turn lane length based on queueing theories and the penetration rate of HDVs. The proposed method is validated using an empirical case study combining established emission models and VISSIM simulation tools and based on field data in a typical intersection. The results show that the proposed method can reduce the CO, HC and NOx emissions at the intersection by around 30% as compared to the original scenario.An enumeration process is conducted to validate further the ability of the proposed method to determine the proper length of left-turn lanes.The optimization of the left-turn lane has similar impacts on the reduction of CO and NOx emissions for LDVs and HDVs but has a more remarkable influence on the HC emissions for HDVs as compared to LDVs.It is found 12 ATTACHMENT L J.Fan et aL Heliyon 9(2023)e16260 that HDVs contribute to most of CO,HC,and NOx emissions at the intersection whilst they take up a small percentage of the traffic flow. The proposed method can also improve traffic efficiency at the intersection by reducing travel delays and queuing,as evidenced by empirical analysis results.This study establishes the instantaneous emission model for mixed traffic flow and provides a model basis for calculating traffic emissions from mixed traffic flows. Moreover, the results provide useful guidance and design methods for trans- portation designers to optimize and improve the left-turn lane configuration to alleviate traffic congestion and reduce traffic emissions at urban intersections. Compared to former studies, this study offers several unique contributions regarding the impact of left-turn lanes on traffic emissions. Firstly, we collected high-resolution field emission and vehicle operating data for both LDVs and HDVs, providing a comprehensive understanding and modelling of the emissions of LDVs and HDVs in different operation conditions,which are essential for analyzing emissions of mixed traffic flow.Secondly,we formulated a tailored model to determine the optimal left-turn lane length, which takes into account queueing theories and the penetration rate of HDVs.Finally,the proposed method was validated using an empirical case study,providing evidence of its effectiveness in reducing emissions and improving traffic efficiency. Nevertheless,there are several limitations that can be further investigated in future work.Firstly,it is tough to directly formulate quantitative models about emissions at the traffic flow level in the optimization model of left-turn lanes,as the change of left-turn lane design will influence several aspects and a lot of vehicles rather than a certain vehicle. It will be interesting work to develop a quantitative method for reflecting the relationship between changes in traffic flow characteristics and corresponding emissions.In this regard, the objective function of the optimization will be more straightforward. Moreover, this study focuses on the left-turn lane design and takes the signal timing setting at the intersection as the default input.However, the signal timing can be optimized and controlled to facilitate traffic efficiency as well,which a load of literature has been doing.It is an interesting future work to jointly optimize lane configuration design and signal timing at the intersection,which is expected to have more remarked benefits.Last but not least,our emission models are established based on the field emission data of three representative vehicles due to data limitations and the high expense of collecting data from various LDVs and HDVs.Collecting more field emission data will always be beneficial for improving the instantaneous emission models and analysis accuracy in relevant studies. Author contribution statement Jieyu Fan: Kun Gao: Conceived and designed the experiments; Performed the experiments;Analyzed and interpreted the data; Wrote the paper.Aoyong Li:Performed the experiments;Analyzed and interpreted the data;Contributed reagents,materials,analysis tools or data;Wrote the paper.Anugrah Ilahi:Performed the experiments;Analyzed and interpreted the data;Wrote the paper. Data availability statement The authors do not have permission to share data. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper- Acknowledgment The authors are grateful to the Area of Advance Transport and Al Center(CHAIR)at the Chalmers University of Technology for funding this research. References [1] X.Qu,Z.Zeng,K.Wang,S.Wang,Replacing urban trucks via ground-air cooperation,Common.Transport.Res.2(2022),100080. 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