It is inevitable that Connected and
Autonomous Vehicles (CAVs) will be a major focus of transportation and the
automotive industry with increased use in future traffic system analysis.
Numerous studies have focused on the evaluation and potential development of
CAVs technology; however, pedestrians and bicyclists, as two essential and
important modes of the road users have seen little to no coverage. In response
to the need for analyzing the impact of CAVs on non-motorized transportation,
this paper develops a new model for the evaluation of the Level of Service
(LOS) for pedestrians in a CAVs environment based on the Highway Capacity
Manual (HCM). The HCM provides a methodology to assess the level of service for
pedestrians and bicyclists on various types of intersections in urban areas.
Five scenarios were created for simulation via VISSIM (a software) that
corresponds to the different proportions of the CAVs and different signal
systems in a typical traffic environment. Alternatively, the Surrogate Safety
Assessment Model (SSAM) was selected for analyzing the safety performance of
the five scenarios. Through computing and analyzing the results of simulation
and SSAM, the latter portion of this paper focuses on the development of a new
model for
evaluating pedestrian LOS in urban areas which are based upon HCM standards
which are suitable for CAVs environments. The results of this study are
intended to inform the future efforts of engineers and/or policymakers and to
provide them with a tool to conduct a comparison of capacity and LOS related to
the impact of CAVs on pedestrians during the process of a transportation system
transition to CAVs.
References
[1]
Li, M. and Faghri, A. (2014) Cost-Benefit Analysis of Added Cycling Facilities. Journal of the Transportation Research Board, 2468, 55-63.
https://doi.org/10.3141/2468-07
[2]
Sandt, L. and Owens, J.M. (2017) Discussion Guide for Automated and Connected Vehicles, Pedestrians, and Bicyclists.
http://www.pedbikeinfo.org/cms/downloads/PBIC_AV_Discussion_Guide.pdf
[3]
Nerwinski, Z., Faghri, A. and Li, M. (2018) Modeling Bicycle Conflict on Non-Mo- torized Paths on Suburban College Campuses. Journal of Transportation Technologies, 8, 357-375. https://doi.org/10.4236/jtts.2018.84020
[4]
Mohammadiziazi, R., Faghri, A. and Li, M. (2018) The Impacts of Sea Level Rise on Non-Motorized Transportation. Transportation Research Board 97th Annual Meeting, Washington DC, January 2018.
[5]
Li, M. and Faghri, A. (2018) A Framework to Analyze the Economic Feasibility of Cycling Facilities. In: Bicycle Urbanism, Routledge, New York, 140-159.
https://doi.org/10.4324/9781315569338-9
[6]
Vaughan, M.L., Faghri, A. and Li, M. (2017) An Interactive Expert System Based Decision Making Model for the Management of Transit System Alternate Fuel Vehicle Assets. Intelligent Information Management, 9, 1-20.
https://doi.org/10.4236/iim.2017.91001
[7]
Li, M., Rouphail, N.M., Mahmoudi, M., Liu, J. and Zhou, X. (2017) Multi-Scenario Optimization Approach for Assessing the Impacts of Advanced Traffic Information under Realistic Stochastic Capacity Distributions. Transportation Research Part C: Emerging Technologies, 77, 113-133. https://doi.org/10.1016/j.trc.2017.01.019
[8]
Vaughan, M.L., Faghri, A. and Li, M. (2018) Knowledge-Based Decision-Making Model for the Management of Transit System Alternative Fuel Infrastructures. International Journal of Sustainable Development & World Ecology, 25, 184-194.
https://doi.org/10.1080/13504509.2017.1333541
[9]
Vlasic, B. and Boudette, N.E. (2016) Self-Driving Tesla Was Involved in Fatal Crash, U.S. Says.
https://www.nytimes.com/2016/07/01/business/self-driving-tesla-fatal-crash-investigation.html
[10]
Newcomer, E. (2018) Uber Puts First Self-Driving Car Back on the Road since Death.
https://www.bloomberg.com/news/articles/2018-12-20/uber-puts-first-self-driving-car-back-on-the-road-since-death
[11]
Green, J. (2018) Tesla: Autopilot Was on during Deadly Mountain View Crash.
https://www.mercurynews.com/2018/03/30/tesla-autopilot-was-on-during-deadly-mountain-view-crash
[12]
Taeihagh, A. and Lim, H.S.M. (2019) Governing Autonomous Vehicles: Emerging Responses for Safety, Liability, Privacy, Cybersecurity, and Industry Risks. Transport Reviews, 39, 103-128. https://doi.org/10.1080/01441647.2018.1494640
[13]
Howard, D. and Dai, D. (2014) Public Perceptions of Self-Driving Cars: The Case of Berkeley, California. Transportation Research Board 93rd Annual Meeting, Washington DC, January 2014, 1-16.
[14]
Fraedrich, E. and Lenz, B. (2014) Automated Driving: Individual and Societal Aspects. Transportation Research Record, 2416, 64-72.
https://doi.org/10.3141/2416-08
[15]
Bollinger, B.L. (2017) The Security and Privacy in Your Car Act: Will It Actually Protect You? North Carolina Journal of Law & Technology, 18, 214-243.
[16]
Marchant, G.E. and Lindor, R.A. (2012) The Coming Collision between Autonomous Vehicles and the Liability System. Santa Clara Law Review, 52, 1321-1340.
[17]
Elias, A. (2011) Automobile-Oriented or Complete Street? Pedestrian and Bicycle Level of Service in the New Multimodal Paradigm. Transportation Research Record, 2257, 80-86. https://doi.org/10.3141/2257-09
[18]
Suarez, R., Faghri, A. and Li, M. (2014) Evaluation of the Accuracy and Automation of Travel Time and Delay Data Collection Methods. Journal of Transportation Technologies, 4, 72-83. https://doi.org/10.4236/jtts.2014.41007
[19]
Li, M. and Faghri, A. (2016) Applying Problem-Oriented and Project-Based Learning in a Transportation Engineering Course. Journal of Professional Issues in Engineering Education and Practice, 142, Article ID: 04016002.
https://doi.org/10.1061/(ASCE)EI.1943-5541.0000274
[20]
Li, M., Faghri, A., Ozden, A. and Yue, Y. (2017) Economic Feasibility Study for Pavement Monitoring Using Synthetic Aperture Radar-Based Satellite Remote Sensing: Cost-Benefit Analysis. Transportation Research Record: Journal of the Transportation Research Board, 2645, 1-11. https://doi.org/10.3141/2645-01
[21]
Raad, N. and Burke, M.I. (2018) What Are the Most Important Factors for Pedestrian Level-of-Service Estimation? A Systematic Review of the Literature. Transportation Research Record, 2672, 101-117.
https://doi.org/10.1177/0361198118790623
[22]
HCM (2000) Highway Capacity Manual. TRB, National Research Council, Washington DC.
[23]
Pandit, K., Ghosal, D., Zhang, H.M. and Chuah, C.N. (2013) Adaptive Traffic Signal Control with Vehicular Ad Hoc Networks. IEEE Transactions on Vehicular Technology, 62, 1459-1471. https://doi.org/10.1109/TVT.2013.2241460
[24]
Li, M., Zhou, X. and Rouphail, N.M. (2017) Quantifying Travel Time Variability at a Single Bottleneck Based on Stochastic Capacity and Demand Distributions. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 21, 79-93. https://doi.org/10.1080/15472450.2016.1163639
[25]
Zhang, K., Zhang, D., de La Fortelle, A., Wu, X. and Gregoire, J. (2015) State-Driven Priority Scheduling Mechanisms for Driverless Vehicles Approaching Intersections. IEEE Transactions on Intelligent Transportation Systems, 16, 2487-2500.
https://doi.org/10.1109/TITS.2015.2411619
[26]
Lin, P., Liu, J., Jin, P.J. and Ran, B. (2017) Autonomous Vehicle-Intersection Coordination Method in a Connected Vehicle Environment. IEEE Intelligent Transportation Systems Magazine, 9, 37-47. https://doi.org/10.1109/MITS.2017.2743167
[27]
Sukennik, P. (2018) Default Behavioural Parameter Sets for AVs. PTV Group.
https://www.h2020-coexist.eu/wp-content/uploads/2018/10/D2.4-Vissim-extension-new-features-and-improvements_final.pdf
[28]
Hamad, K., Faghri, A. and Li, M. (2015) Forecasting Model for Vehicular Demand: An Alternative Methodology in the Context of Developing Countries. The Journal of Developing Areas, 49, 125-143. https://doi.org/10.1353/jda.2015.0006
[29]
Wu, J. (2017) Analysis of Pedestrian Safety Using Micro-Simulation and Driving Simulator. University of Central Florida, Orlando.
[30]
Wahed, M., Faghri, A. and Li, M. (2017) An Innovative Simulation Model for the Operations of a Multipurpose Seaport: A Case Study from Port of Wilmington, USA. International Journal of Simulation and Process Modelling, 12, 151-164.
https://doi.org/10.1504/IJSPM.2017.083530