Trajectories Of Road Users Research Articles

Crash frequency prediction based on extreme value theory using roadside lidar-based vehicle trajectory data

Crash prediction models (CPMs) are mostly developed using statistical or data-driven methods that rely on observed crashes. However, the historical crash records can be unreliable due to availability and data quality issues. Near-crashes based CPMs offer a proactive approach to predict crash frequencies prior to the occurrence of crashes. Surrogate safety measures can be used to identify near-crashes from road user trajectories. Roadside LiDAR offers an innovative approach to collect vehicle trajectory data at a microscopic resolution with high accuracy providing detailed information of all road user movements. This study presents a methodology to identify near-crashes from Roadside LiDAR based vehicle trajectory data using the surrogate indicators: TTC (Time to Collision), PET (Post Encroachment Time), ACT (Anticipated Collision Time) and MaxD (Maximum Deceleration). Additionally, time-based, and evasive-action-based surrogate measures are combined as different pairs to obtain crash probabilities using extreme value theory (EVT). The study results show that the bivariate EVT model displays a better fit to conflict extremes, predicting crash frequencies better than the univariate model. Likewise, while the bivariate model with ACT and MaxD pair performed the best in terms of accuracy, the TTC and MaxD pair was able to reflect the relative threat levels at the study intersections. Overall, the methodology lays ground for using roadside lidar based trajectory data for proactive safety analysis of signalized intersections.

Using spatiotemporal stacks for precise vehicle tracking from roadside 3D LiDAR data

This paper develops a non-model based vehicle tracking methodology for extracting road user trajectories as they pass through the field of view of a 3D LiDAR sensor mounted on the side of the road. To minimize the errors, our work breaks from conventional practice and postpones target segmentation until after collecting LiDAR returns over many scans. Specifically, our method excludes all non-vehicle returns in each scan and retains the ungrouped vehicle returns. These vehicle returns are stored over time in a spatiotemporal stack (ST stack) and we develop a vehicle motion estimation framework to cluster the returns from the ST stack into distinct vehicles and extract their trajectories. This processing includes removing the impacts of the target's changing orientation relative to the LiDAR sensor while separately taking care to preserve the crisp transition to/from a stop that would normally be washed out by conventional data smoothing or filtering. This proof of concept study develops the methodology using a single LiDAR sensor, thus, limiting the surveillance region to the effective range of the given sensor. It should be clear from the presentation that, provided sufficient georeferencing, the surveillance region can be extended indefinitely by deploying multiple LiDAR sensors with overlapping fields of view.

Real-time safest route identification: Examining the trade-off between safest and fastest routes

Several studies have shown that crash risk is a dynamic quantity that is frequently changing with considerable spatial and temporal variations. Recent advances in safety evaluation techniques such as using extreme value theory (EVT) models provided the opportunity to use traffic conflict data obtained from road user trajectories to estimate real time safety metrics. These metrics can aggregate crash risk along a certain route based on the duration of exposure to unsafe road conditions. This paper applies a Bayesian hierarchal extreme value theory model to trajectories obtained from a drone dataset from Athens, Greece, to develop a safest route algorithm capable of informing users about the safest route in an urban network in real time. The study area selected consists of a rectangular grid made up of 102 signalized and unsignalized intersections. The dynamic crash risk for each link in the network was obtained and used to identify the safest route between any origin–destination pair and the corresponding fastest route. The safest routes were then compared to the fastest routes and were found to be 22% safer on average, resulting in an 11% increased travel time. Moreover, the safest route was identical to the fastest route in 23% of the origin–destination pairs analyzed and had an average similarity of 54% in terms of links. Recognizing the trade-off between safety and mobility, a multi-objective routing methodology was proposed which balances travel time and crash risk using a weighted preference for safety. This work has considerable potential for improving the safety of all road users and may also be used for fleet routing applications as part of multi-objective routing systems.

Leading pedestrian intervals – Yay or Nay? A Before-After evaluation of multiple conflict types using an enhanced Non-Stationary framework integrating quantile regression into Bayesian hierarchical extreme value analysis

A pedestrian was estimated to be killed every 85 min and injured every 7 min on US roads in 2019. Targeted safety treatments are particularly required at urban intersections where pedestrians regularly conflict with turning vehicles. Leading Pedestrian Intervals (LPIs) are an innovative, low-cost treatment where the pedestrian and vehicle usage of the potential conflict area (a crosswalk) is staggered in time to give the pedestrians a head start of a few seconds and reduce the “element of surprise” for right-turning vehicles. The effectiveness of LPI treatment on pedestrian safety is mixed, and most importantly, its effect on vehicle-vehicle conflicts is unknown. This study investigates the before-after effects of LPI treatments on vehicle–pedestrian and vehicle-vehicle crash risk by applying traffic conflict techniques. In particular, this study has developed a quantile regression technique within the extreme value model to estimate and compare crash risks before and after the installation of the LPI treatment. The before-after traffic movement video data (504 h in total) were collected from three signalized intersections in the City of Bellevue, Washington. The recorded movements were analyzed using Microsoft’s proprietary computer vision platform, Edge Video Service, and Advanced Mobility Analytics Group’s cloud-based SMART SafetyTM platform to automatedly extract traffic conflicts by analyzing road user trajectories. The treatment effect was measured using a Bayesian hierarchical extreme value model with the peak-over threshold approach. For the extreme value model, a Bayesian quantile regression analysis was conducted to estimate the conflict thresholds corresponding to a high (95th) quantile. Odds ratios were estimated for both conflict types using untreated crossing as a control group. Results indicate that the LPI treatment reduces the crash risk of pedestrians as measured by the reduction in extreme vehicle–pedestrian conflicts by about 42%. The LPI treatment has also been found not to negatively affect rear-end conflicts along the approaches leading to the LPI-treated pedestrian crossing at the signalized intersections. The findings of this study further emphasize the effectiveness of video analytics in proactive safety evaluations of engineering treatments.

Do road users play Nash Equilibrium? A comparison between Nash and Logistic stochastic Equilibriums for multiagent modeling of road user interactions in shared spaces

Analyzing and modeling multiagent transportation systems such as cyclist-pedestrian interactions and their evasive-action mechanisms in shared spaces are important for operation and safety evaluations. However, there are limited studies that (1) modeled the multiagent nature of road user interactions and their concurrent sequential decision processes, and (2) investigated the ability of different equilibrium behavioral theories in predicting road user operational-level decisions and evasive-action mechanisms. This study proposes two novel multiagent approaches based on different equilibrium theories for modeling road user interactions: (1) the Multiagent Generative Adversarial Imitation Learning (MAGAIL), which utilizes Nash-Equilibrium (NE) theory that assumes fully rational and optimal road user behavior, and (2) the Multiagent Adversarial Inverse Reinforcement Learning (MAAIRL), which utilizes Logistic-Stochastic-Best-Response-Equilibrium (LSBRE) theory that handles bounded rationality (sub-optimal) behavior. Unlike many of the traditional game-theoretic modeling approaches, which consider single time-step payoff modeling, the proposed approaches depend on Markov-Game that accounts for the stochastic nature of road user interactions and their sequential decision processes. The models recover road user multiagent reward functions and estimate their strategies using Multiagent Deep Reinforcement Learning. Using trajectories from three shared spaces in the USA and Canada, the study compared the proposed approaches’ results and determined a behavior-based consistent paradigm to model equilibrium in multiagent transportation systems. The results show that LSBRE-based model predicted road user trajectories and their evasive action mechanisms with higher accuracy compared to the NE-based model.

Transferability of multivariate extreme value models for safety assessment by applying artificial intelligence-based video analytics

Traffic conflict techniques represent the state-of-the-art for road safety assessments. However, the lack of research on transferability of conflict-based crash risk models, which refers to applying the developed crash risk estimation models to a set of external sites, can reduce their appeal for large-scale traffic safety evaluations. Therefore, this study investigates the transferability of multivariate peak-over threshold models for estimating crash frequency-by-severity. In particular, the study proposes two transferability approaches: (i) an uncalibrated approach involving a direct application of the uncalibrated base model to the target sites and (ii) a threshold calibration approach involving calibration of conflict thresholds of the conflict indicators. In the latter approach, the conflict thresholds of the Modified Time-To-Collision (MTTC) and Delta-V indicators were calibrated using local data from the target sites. Finally, the two transferability approaches were compared with a complete re-estimation approach where all the model parameters were estimated using local data. All three approaches were tested for a target set of signalized intersections in Southeast Queensland, Australia. Traffic movements at the target intersections were observed using video cameras for two days (12 h each day). The road user trajectories and rear-end conflicts were extracted using an automated artificial intelligence-based algorithm utilizing state-of-the-art Computer Vision methods. The base models developed in an earlier study were then transferred to the target sites using the two transferability approaches and the local data from the target sites. Results show that the threshold calibration approach provides the most accurate and precise predictions of crash frequency-by-severity for target sites. Thus, for peak-over threshold models, the threshold parameter is the most important, and its calibration improves the performance of the base models. The complete re-estimation of models for individual target sites yields inferior fits and less precise crash estimates than the two transferability approaches since they utilize fewer traffic conflict extremes in their development than the larger dataset utilized in base model development. Therefore, the study results can significantly advance the applicability of traffic conflict models for crash risk estimation at transport facilities.

Interactions Between Heavy Trucks and Vulnerable Road Users-A Systematic Review to Inform the Interactive Capabilities of Highly Automated Trucks.

This study investigates interactive behaviors and communication cues of heavy goods vehicles (HGVs) and vulnerable road users (VRUs) such as pedestrians and cyclists as a means of informing the interactive capabilities of highly automated HGVs. Following a general framing of road traffic interaction, we conducted a systematic literature review of empirical HGV-VRU studies found through the databases Scopus, ScienceDirect and TRID. We extracted reports of interactive road user behaviors and communication cues from 19 eligible studies and categorized these into two groups: 1) the associated communication channel/mechanism (e.g., nonverbal behavior), and 2) the type of communication cue (implicit/explicit). We found the following interactive behaviors and communication cues: 1) vehicle-centric (e.g., HGV as a larger vehicle, adapting trajectory, position relative to the VRU, timing of acceleration to pass the VRU, displaying information via human-machine interface), 2) driver-centric (e.g., professional driver, present inside/outside the cabin, eye-gaze behavior), and 3) VRU-centric (e.g., racer cyclist, adapting trajectory, position relative to the HGV, proximity to other VRUs, eye-gaze behavior). These cues are predominantly based on road user trajectories and movements (i.e., kinesics/proxemics nonverbal behavior) forming implicit communication, which indicates that this is the primary mechanism for HGV-VRU interactions. However, there are also reports of more explicit cues such as cyclists waving to say thanks, the use of turning indicators, or new types of external human-machine interfaces (eHMI). Compared to corresponding scenarios with light vehicles, HGV-VRU interaction patterns are to a high extent formed by the HGV’s size, shape and weight. For example, this can cause VRUs to feel less safe, drivers to seek to avoid unnecessary decelerations and accelerations, or lead to strategic behaviors due to larger blind-spots. Based on these findings, it is likely that road user trajectories and kinematic behaviors will form the basis for communication also for highly automated HGV-VRU interaction. However, it might also be beneficial to use additional eHMI to compensate for the loss of more social driver-centric cues or to signal other types of information. While controlled experiments can be used to gather such initial insights, deeper understanding of highly automated HGV-VRU interactions will also require naturalistic studies.

Do stop-signs improve the safety for all road users? A before-after study of stop-controlled intersections using video-based trajectories and surrogate measures of safety

Converting minor-approach-only stop (MAS) intersections to all-way-stop (AWS) intersections is a prevailing safety countermeasure in North American urban areas. Although the general population positively perceives the installation of stop-signs in residential areas, little research has investigated the impact of AWS on road safety and road user behaviour. This paper investigated the safety effectiveness of converting MAS to AWS intersections using an observational before and after approach and surrogate measures of safety. More specifically, the safety impacts of AWS conversion were investigated using multiple indicators, including vehicle speed measures, vehicle–pedestrian, vehicle-cyclist, vehicles-vehicle interactions as well as yielding rates before and after the treatment implementation. A multi-level regression approach was adopted to determine the effect of stop signs controlling for built environments, traffic exposure, and intersection geometry factors as well as site-specific unobserved heterogeneity.A unique sample of 31 intersections were used in this before-after study. From this sample, video data were collected before and after implementing AWS. In total, 245 h of video were automatically processed and corrected using a specialized computer vision software. More than 68,000 (37,668 before and 31,305 after AWS treatment) road user trajectories were obtained from 104 approaches. The results show that the conversion of MAS to AWS intersections significantly decreased vehicle speed and increased post-encroachment time. This work also shows that implementing AWS significantly increased the yielding rates from 45.7% to 76.7% in MAS conditions and reduced the average speed of motor-vehicles. Using multi-level regression model, it is estimated that when the intersection was converted from MAS to AWS, the minimum speed in the major approaches was reduced by 60.0%.

Study of automated shuttle interactions in city traffic using surrogate measures of safety

Driving automation is happening at a rapid pace, with different driver assistance systems already available in mass-market cars. However, this rapid development in driving automation leads to concerns and questions about their impact on safety. While previous studies have been restricted to incident reports and simulation tools, the safety of automated vehicles (AVs) has not been clearly demonstrated independently from their developers and proponents. Instead of crashes, which are extremely rare events, this study uses surrogate measures of safety (SMoS) to analyze the interactions between road users and low-speed automated shuttles that circulated in Montreal and Candiac, in Canada, during two pilot projects in mid and late 2019. Cameras were placed at seven intersections along the routes of the shuttles. More than 70 hours of footage were processed to extract the road user trajectories using computer vision techniques and compute various safety indicators: speed and acceleration at the road user level, time headway, time-to-collision (TTC), post-encroachment time (PET) and the speed difference at the interaction level. The Kolmogorov–Smirnov test was used to compare the distributions of these indicators for interactions involving automated shuttles or motorized vehicles following similar paths. Multivariate regression was used to identify the relationship between the safety indicators and various factors. The results indicate that these automated shuttles behave generally more safely: their speeds and accelerations are lower and their interactions are characterized by higher TTC and PET values, and lower speed differences. However, small headway times at one site with high-speed differences between the shuttles and other following vehicles raise concerns that warrant further research into the suitable context for these vehicles.

Can motorcyclist behavior in traffic conflicts be modeled? A deep reinforcement learning approach for motorcycle-pedestrian interactions

Traffic simulation models have been utilized recently in safety evaluations by calculating traffic conflict indicators from simulated road user trajectories. However, existing simulation models (1) do not accurately capture road users’ behaviour and evasive actions, and (2) do not consider road users’ intelligence and rationality. This study proposes a safety-oriented microsimulation framework for modeling conflict interactions between motorcyclists and pedestrians in mixed traffic conditions. Motorcyclists are modeled as utility-based rational and intelligent decision-makers using a Markov Decision Process (MDP). Continuous Inverse Reinforcement Learning (IRL) is proposed to recover the motorcyclists’ reward function using their actual trajectories. The recovered motorcyclists’ reward function provides inferences into their behavior in conflict interactions. The motorcyclists’ optimal policies (sequences of decisions) are estimated using the Actor-Critic Deep Reinforcement Learning. The results show that the model simulated motorcyclist trajectories and the evasive actions with high accuracy. Moreover, the predicted PETs correlated well with corresponding field-measured conflicts.

Online risk estimation of critical and non-critical interactions between right-turning motorists and crossing cyclists by a decision tree

One of the most critical situations in urban areas is when motorists turn right in an intersection and cyclists cross the road. Many of those crashes result in severe consequences for cyclists. In order to increase the safety of cyclists, especially in the case of conflicts with right-turning vehicles, an online infrastructure-based assistance system may be a promising solution warning drivers and cyclists when a conflict or crash is predicted. By means of automated video traffic detection, the resulting trajectories of road users can be analysed and a warning can be sent to vehicles and cyclists equipped with vehicle-to-anything communication (V2X) when a high risk is estimated. An approach for online risk estimation was developed combining the surrogate measure of safety (SMoS) gap time (GT) with trajectory prediction-based estimates of the time-to-arrival (TTA) or distance to conflict point (DCp) and velocity (v). A decision tree as classifier of risk levels based on the previous named risk features was trained to model the risks perceived by humans. Expert ratings of traffic conflict scenes were used to build a model, apply the model, and improve it in the field. The warning system was evaluated by test drives in real traffic at the urban AIM Research Intersection in Braunschweig, Germany. In general, the system warned reliably. In approximately 67% of the trials, it was assessed as helpful.

Modeling lateral interactions between motorized vehicles and non-motorized vehicles in mixed traffic using accelerated failure duration model

The objective of this study is to model the lateral interactions between motorized vehicles (MVs) and non-motorized vehicles (NMVs) in mixed traffic. Road user trajectories from two locations in China are extracted using computer vision techniques. The critical lateral distance (the shortest lateral distance to initiate avoidance maneuvers) is used as the lateral interaction indicator. Lateral interactions are modelled using the parametric accelerated failure time (AFT) duration model with a Weibull distribution, and the unobserved heterogeneity is considered using gamma frailty. The results show that interaction probabilities increase at higher MV speeds or NMV-MV speed differences and decrease with the NMV or MV yaw rates. The critical lateral distances when NMV ride in the MV lanes are shorter than those in the NMV lane. Moreover, bikes have higher interaction probabilities than e-bikes. These findings give insights into lateral interaction behaviours in mixed traffic and support better designs of such facilities.

Microscopic modeling of cyclists interactions with pedestrians in shared spaces: a Gaussian process inverse reinforcement learning approach

This study presents a microsimulation-oriented framework for modeling cyclists' interactions with pedestrians in shared spaces. The objectives of this study are to 1) infer how cyclists in head-on and crossing interactions rationally assess and make guidance decisions of acceleration and yaw rate, and 2) use advanced Artificial Intelligent (AI) techniques to model road-user interactions. The Markov Decision Process modeling framework is used to account for road-user rationality and intelligence. Road user trajectories from three shared spaces in North America are extracted by means of computer-vision algorithms. Inverse Reinforcement Learning (IRL) algorithms are utilized to recover continuous linear and nonlinear Gaussian-Process (GP) reward-functions (RFs). Deep Reinforcement Learning is used to estimate optimal cyclist policies. Results demonstrated that the GP-RF captures the more complex interaction behaviour and accounts for road-user heterogeneity. The GP-RF led to more consistent inferences of road-users behaviour and accurate predictions of their trajectories compared with the linear RF.

Road Boundary-Enhanced Automatic Background Filtering for Roadside Lidar Sensors

The roadside-deployed lidar sensor provides a solution to obtain the real-time, high-resolution micro traffic data (HRMTD) of all road users in the mixed traffic situation (connected vehicles and unconnected vehicles both exist on the roads). Background filtering is a necessary and important step for the HRMTD collection to serve the connected vehicle. Without excluding the background points, the accuracy of Euclidean-based object clustering and tracking algorithms can be reduced. The widely used method-3D-density-statistic filtering (3D-DSF) for roadside lidar background filtering can effectively exclude the background points for free-flow conditions. However, the performance of 3D-DSF can be greatly influenced by congested traffic conditions. This article presents a revised 3D-DSF algorithm to automatically extract background points by involving the road-boundary information. This new method is named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">road-boundary-enhanced, 3D-density statistic filtering</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3D-DSFRB</i> ). This algorithm involves the boundary of the historical trajectories of road users as the region of interest (ROI) to enhance the accuracy of background filtering. A revised grid-based method was developed for road-boundary ID. The 3D-DSF was only applied for the area outside of the ROI. Within the ROI, only ground surface was excluded. Case studies were conducted to evaluate the effectiveness of the 3D-DSFRB algorithm. The results showed that the 3D-DSFRB can filter background points for both free-flow conditions and congested traffic conditions. The time cost of the 3D-DSFRB was also reduced compared to the 3D-DSF. Compared to the state of the art, the 3D-DSFRB improved the accuracy of background filtering.

TRAJECTORY EXTRACTION FOR ANALYSIS OF UNSAFE DRIVING BEHAVIOUR

Abstract. The environment of the vehicle can significantly influence the driving situation. Which conditions lead to unsafe driving behaviour is not always clear, also not to a human driver, as the causes might be unconscious, and thus cannot be revealed by expert interviews. Therefore, it is important to investigate how such situations can be reliably detected, and then search for their triggers. It is conceivable that such insecure situations (e.g. near-accidents, U-turns, avoiding obstacles) are reflected, for example, as anomalies in the movement trajectories of road users.Collecting real world traffic data in driving studies is very time consuming and expensive. However, a lot of roads or public areas are already monitored with video cameras. In addition, nowadays more and more of such video data is made publicly available over the internet so that the amount of free video data is increasing. This research will exploit the use of such kind of opportunistic VGI. In the paper the first step of an automatic analysis are presented, namely: to introduce a real time processing pipeline to extract road user trajectories from surveillance video data.

Automatic Vehicle-Pedestrian Conflict Identification With Trajectories of Road Users Extracted From Roadside LiDAR Sensors Using a Rule-Based Method

Vehicle-pedestrian conflicts have been the major concern for traffic safety. Surrogate safety measures are widely applied for pedestrian safety evaluation. However, how to quickly identify the vehicle-pedestrian surrogate safety measures at the individual site is challenging due to the difficulty of obtaining the high-resolution trajectories of road users. This paper presented an effective method to generate the high-resolution traffic trajectories from the roadside deployed Light Detection and Ranging (LiDAR) sensor. The vehicle-pedestrian conflicts can then be identified from the trajectories simply using the speed-distance profile (SDP) of the vehicles. The SDP can be used to develop a rule-based method for vehicle-pedestrian identification. The events can be divided into different risk levels based on the spatial distribution of the SDP. The case study shows that the rule-based method can detect vehicle-pedestrian near-crash events effectively. The other indicators, such as widely used time-to-collision (TTC) or deceleration rate to avoid a crash (DRAC), can be also obtained from the SDP. The engineers can also adjust the thresholds in the rule-based method to meet the specific requirements at different sites. The proposed method can be extended to identify vehicle-vehicle conflicts or vehicle-bicycle conflicts in future studies.

A novel method of vehicle-pedestrian near-crash identification with roadside LiDAR data

Safety evaluation based on historical crashes usually has a lot of limitations. In previous studies, near-crashes are considered as surrogate data for safety evaluation. One challenge for the use of near-crashes data is the difficulty of data collection. The driving simulators and naturalistic driving data may not be suitable for safety evaluation at specific sites. The observational site-based methods such as human observers and video analysis also suffer from some limitations such as long time data processing or reduced performance influenced by weather or light condition. The roadside Light Detection and Ranging (LiDAR)-enhanced infrastructure provides a new solution for real-time data collection without the impact from weather or light. The high-resolution trajectories of all road users can be obtained from roadside LiDAR data. This paper aims to fill these gaps by presenting a method for near-crash identification based on the trajectories of road users extracted from roadside LiDAR data. This paper focused on vehicle-pedestrian near-crash identification particularly considering the increased risk of vehicle-pedestrian conflicts. Three parameters: Time Difference to the Point of Intersection (TDPI); Distance between Stop Position and Pedestrian (DSPP); Vehicle-pedestrian speed-distance profile, were developed for vehicle-pedestrian near-crash identification. The authors also recommended the thresholds for risk assessment of pedestrian safety. This method was coded into an automatic procedure for near-crash identification. This method is expected to significantly improve the current evaluation of pedestrian safety.

Analysing cyclist behaviour at cycling facility discontinuities using video data

The primary purpose of any transportation network is to provide connectivity between the origin and travel destination. However, given the vehicle oriented structure of the road network in many countries, there are connectivity issues in the cycling network, which has been implemented later. Discontinuities are physical interruptions in the cycling network where cyclists are faced with unexpected situations such as the end of a cycling facility or the change from one facility type to another that are perceived as inconvenient and less safe. The microscopic behaviour of cyclists and the risks they face at these points of discontinuity has not been extensively investigated in the literature. This study aims to evaluate the challenges faced by cyclists at discontinuities by observing cyclist behaviour at these locations and comparing them to control sites using automated video analysis techniques. Our methodology allows the extraction of valuable microscopic data for evaluation of cyclist behaviour at any location. The methodology is applied to a case study of four sites in Montreal, Canada. Using a set of discontinuity measures proposed in a previous work and applied to Montreal's cycling network, video data was collected from a pole-mounted camera at locations with discontinuity and control sites. After extracting road user trajectories from the video data, a trajectory clustering algorithm was applied to find cyclists' motion patterns and the various maneuver strategies adopted by cyclists. Speeds and acceleration statistics are extracted and compared between different motion patterns and between discontinuity and control sites. Results show that cyclists undertake a larger number of maneuvers at points of discontinuity compared to their control sites, and that both cyclist accelerations and speeds exhibit larger variations at discontinuities compared to larger and more stable speeds at control sites.

Investigating the gender differences on bicycle-vehicle conflicts at urban intersections using an ordered logit methodology

In the literature, a crash-based modeling approach has long been used to evaluate the factors that contribute to cyclist injury risk at intersections. However, this approach has been criticized as crashes are required to occur before contributing factors can be identified and countermeasures can be implemented. Moreover, human factors related to dangerous behaviors are difficult to evaluate using crash-based methods. As an alternative, surrogate safety measures have been developed to address the issue of reliance on crash data. Despite recent developments, few methodologies and little empirical evidence exist on bicycle-vehicle interactions at intersections using video-based data and statistical analyses to identify associated factors. This study investigates bicycle-vehicle conflict severity and evaluates the impact of different factors, including gender, on cyclist risk at urban intersections with cycle tracks. A segmented ordered logit model is used to evaluate post-encroachment time between cyclists and vehicles. Video data was collected at seven intersections in Montreal, Canada. Road user trajectories were automatically extracted, classified, and filtered using a computer vision software to yield 1514 interactions. The discrete choice variable was generated by dividing post-encroachment time into normal interactions, conflicts, and dangerous conflicts. Independent variables reflecting attributes of the cyclist, vehicle, and environment were extracted either automatically or manually. Results indicated that an ordered model is appropriate for analyzing traffic conflicts and identifying key factors. Furthermore, exogenous segmentation was beneficial in comparing different segments of the population within a single model. Male cyclists, with all else being equal, were less likely than female cyclists to be involved in conflicts and dangerous conflicts at the studied intersections. Bicycle and vehicle speed, along with the time of the conflict relative to the red light phase, were other significant factors in conflict severity. These results will contribute to and further the understanding of gender differences in cycling within North America.

Modeling of Shared Space with Multi-modal Traffic using a Multi-layer Social Force Approach

In the field of traffic road design, the shared space approach aims to develop roads from mere traffic infrastructures to public spaces, compelling higher interaction between road users. In this paper we develop the fundamentals for a micro-simulation tool based on the Social Force Model, to represent the motion of road users in such layouts. Working with the observed behavior of users in a pedestrian-friendly intersection in the city of Braunschweig (D), a multi-layer structured model is developed, in which each layer is designated to handle different situations, from free-flow movements to user interactions in crowded situations. Visibility graphs and clothoid estimations are used for designing trajectories of road users for the free flow movement. Furthermore, an enhancement of the classical Social Force Model is provided in order to model long-range collision avoidance behavior. Finally, the enhanced simulation framework is validated by two observed scenarios, which include various conflicts between pedestrians and cars.