Computational interaction models for automated vehicles and cyclists

Cyclists’ safety is crucial for a sustainable transport system. Cyclists are considered vulnerableroad users because they are not protected by a physical compartment around them. In recentyears, passenger car occupants’ share of fatalities has been decreasing, but that of cyclists hasactually increased. Most of the conflicts between cyclists and motorized vehicles occur atcrossings where they cross each other’s path. Automated vehicles (AVs) are being developedto increase traffic safety and reduce human errors in driving tasks, including when theyencounter cyclists at intersections. AVs use behavioral models to predict other road user’sbehaviors and then plan their path accordingly. Thus, there is a need to investigate how cyclistsinteract and communicate with motorized vehicles at conflicting scenarios like unsignalizedintersections. This understanding will be used to develop accurate computational models ofcyclists’ behavior when they interact with motorized vehicles in conflict scenarios.The overall goal of this thesis is to investigate how cyclists communicate and interact withmotorized vehicles in the specific conflict scenario of an unsignalized intersection. In the firstof two studies, naturalistic data was used to model the cyclists’ decision whether to yield to apassenger car at an unsignalized intersection. Interaction events were extracted from thetrajectory dataset, and cyclists’ behavioral cues were added from the sensory data. Bothcyclists’ kinematics and visual cues were found to be significant in predicting who crossed theintersection first. The second study used a cycling simulator to acquire in-depth knowledgeabout cyclists’ behavioral patterns as they interacted with an approaching vehicle at theunsignalized intersection. Two independent variables were manipulated across the trials:difference in time to arrival at the intersection (DTA) and visibility condition (field of viewdistance). Results from the mixed effect logistic model showed that only DTA affected thecyclist’s decision to cross before the vehicle. However, increasing the visibility at theintersection reduced the severity of the cyclists’ braking profiles. Both studies contributed tothe development of computational models of cyclist behavior that may be used to support safeautomated driving.Future work aims to find differences in cyclists’ interactions with different vehicle types, suchas passenger cars, taxis, and trucks. In addition, the interaction process may also be evaluatedfrom the driver’s perspective by using a driving simulator instead of a riding simulator. Thissetup would allow us to investigate how drivers respond to cyclists at the same intersection.The resulting data will contribute to the development of accurate predictive models for AVs

UMass Amherst CMP Transportation and Parking Appendix A

The University of Massachusetts Transportation Study is a key component of the University of Massachusetts Amherst Campus Master Plan 2012. Produced by Vanesse Hangen Brustlin, Inc. (VHB), this study focuses on the transportation component of the Master Plan. The purpose of the Transportation Study is to describe existing conditions, project future conditions with development proposed in the Master Plan, identify existing and future transportation deficiencies, and recommend enhancements and improvements to improve safety and system operations. The Transportation Study includes traffic operations (traffic counts, turning movements, intersection performance, etc.), pedestrian and bicycle accommodations and transit services. The study is divided into two main sections: The Campus Today and The Future Campus. The framework for analysis of the UMass transportation system follows the following five principles: i) Think Pedestrian First, ii) Complete the Bicycle Network, iii) Enhance Transit Connections, 4) Complete the Streets, 5) Managing Traffic Effectively

Comparative analysis & modelling for riders’ conflict avoidance behavior of E-bikes and bicycles at un-signalized intersections

With the increasing popularity of electric-assist bikes (E-bikes) in China, U.S. and Europe, the corresponding safety issues at intersections have attracted the attention of researchers. Understanding the microscopic behavior of E-bike riders during conflicts with other road users is fundamental for safety improvement and simulation modeling of E-bikes at intersections. This study compared the conflict avoidance behaviors of E-bike and conventional bicycle riders using field data extracted from video recordings of different intersections. The impact of conflicting road user type and gender on E-bikes and bicycles were analyzed. Compared with bicycles, E-bikes appeared to enable more flexibility in conflict avoidance behavior. For example, E-bikes would behave like bicycles when conflicting with motor vehicles/Ebikes, and behave more like motor vehicles when conflicting with bicycles/pedestrians. Based on this, we built an Extended Cyclist Conflict Avoidance Movement (ECCAM) model, which can represent the conflict avoidance behavior of E-bikes/bicycles at mixed traffic flow un-signalized intersections. Field data were applied to validate the proposed model, and the results are promising

ASSESSING THE IMPACT OF BICYCLE TREATMENTS ON BICYCLE SAFETY: A MULTI-METHODS APPROACH

Compared to other modes, bicyclists are disproportionally affected by crashes considering their low mode share. There is evidence that crashes between bicyclists and motorized vehicle take place at road segments and signalized intersections where bicycle treatments (e.g., bike lanes) are present, urging for in-dept analysis of the safety impact of the various bicycle treatment types. Additionally, it is important to identify sensor types that have the potential to advance field data collection and traffic monitoring in multi-modal road environments. In this dissertation, three approaches, namely crash analysis, traffic conflict analysis, and analysis of driver speeding and glancing behavior, were implemented to investigate the safety impact of bicycle treatments at the segment- and the intersection-levels on bicycle safety. Prediction models were developed to predict bicycle-motorized vehicle crashes at road segments and signalized intersections, and traffic conflicts between straight-going bicyclists and right-turning vehicles at signalized intersections. Driver speeding and glancing behavior was analysed for the segment and the intersection levels. A mode classification framework to classify trajectories recorded using a radar-based sensor was developed to test the feasibility of using radar-based sensors in field studies. The findings of this dissertation contribute to bicycle safety research in terms of quantifying the safety impact of various bicycle treatment types and how to assess and also, by showing how to assess bicycle safety. The findings of this research have the potential to stand as a valuable tool for transportation policymakers and officials in charge of establishing safe bicycle networks

Operational analysis of motorized and non-motorized vehicle flow at intersection of Changying anf Wanshow roads

With the rapid development of society, the transportation is more and more important in our country. That's why more and more traffic problems need to solve. Speed limit in the urban area at Xi'an city is already quite low at 40km/h, and this posses major problem at intersection where the traffic has to slow down at least by 50% of the speed limit as to allow vehicle from other directions to pass through; worst still having mixed mode of motorized and non- motorized vehicles at the same time The better to solve these problems are improving the road network and the traffic system. For this project, to determine the limit of capacity for the intersection based on three scenarios (mixed mode, bus and other motorized vehicles and bicycle only) as to analysis the severity of the problem According to these conditions the project will show the best intersection for the case. This research can apply the modern road network and improve the traffic system by analysis and design the new intersection, the suitable design can save cost, increase the speed through the intersection, and fully utilize space area. some common traffic problems can be relieved

Network-Wide Pedestrian and Bicycle Crash Analysis with Statistical and Machine Learning Models in Utah

Recent trends in crashes indicate a dramatic increase in both the number and share of pedestrian and bicyclist injuries and fatalities nationally and in many states. Crash frequency modeling was undertaken to identify crash prone characteristics of segments and non-signalized intersections and explore possible non-linear associations of explanatory variables with crashes. Crowdsourced “Strava” app data was used for bicycle volume, and pedestrian counts estimated from nearby signalized intersections were used as pedestrian volume. Multiple negative binomial models investigated crashes at different spatial scales to account for different levels of data availability and completeness. The models showed high traffic volume, steeper vertical grades on roads, frequent bus and rail stations, greater driveway density, more legs at intersections, streets with high large truck presence, greater residential and employment density, as a larger share of low-income households and non-white race/ethnicity groups are indicators of locations with more pedestrian and bicycle crashes. Crash severity model results showed that crashes occurring at mid-blocks and near vertical grades were more severe compared to crashes at intersections. High daily temperature, driving under influence, and distracted driving also increases injury severity in crashes. This study suggests potential countermeasures, policy implications, and the scope of future research for improving pedestrian and bicycle safety at segments and at non-signalized intersections

Updated Methods for Traffic Impact Analysis, Including Evaluation of Innovative Intersection Designs: Volume II—Applicant’s Guide

The INDOT Applicant’s Guide to Traffic Impact Analysis (TIA) is a product of SPR-3605 Updated Methods for Traffic Impact Analysis . The purpose of this study was to review the Applicant’s and Reviewer’s Guides that were published in 1992 and make changes that would bring them in line with the methods and conditions that have emerged since then. This guide is intended to establish a standard framework for traffic impact analysis within Indiana, increasing consistency in study requests, preparation and review. A standardized procedure will enable the TIA study preparer to present the study findings and recommendations in a systematic manner consistent with the reviewer\u27s expectations. The guide is not intended to make things more complicated and time-consuming. On the contrary, with a standard framework, the time involved in the process will decrease for both parties. The Applicant\u27s Guide allows enough flexibility to the study preparer to use innovative methods based on sound engineering judgment and the conditions at a specific site. However, this should be done with the prior consent of the study reviewer(s)

Simulated Conflict Based Safety Evaluation Models for Hetergenous Traffic in Controlled Intersections

In this paper, an attempt is made to investigate how traffic conflicts identified from microsimulation models can be correlated with explanatory variables which have been traditionally used in accident prediction models. In developing countries with heterogenous traffic streams, availability of accident data is limited especially since accidents are rare events.  Such traffic streams normally have some unique attributes like absence of lane discipline, presence of non-motorized vehicles. In urban intersections with such slow-moving traffic streams, conflicts are more useful determinants of intersection safety rather than previous records of accidents since geometry of intersection may be changed from the time to time. Simulated conflict-based safety evaluation models were developed for intersections of Dhaka city. The intersections were modeled in VISSIM after suitable calibration, for 8 hours of peak hour traffic. Surrogate Safety Assessment Model (SSAM) was used to identify the corresponding simulated hourly conflicts from the resulting trajectory files. It was found that hourly simulated conflicts had a significant statistical relationship with observed hourly traffic volume entering the intersection from major and minor roads. Increasing volumes of non-motorized traffic was found to contribute to intersection safety