Impacts of information quantity and display formats on driving behaviors in a connected vehicle environment.

The emerging connected vehicle (CV) technologies facilitate the development of integrated advanced driver assistance systems (ADASs), with which various functions are coordinated in a comprehensive framework. However, challenges arise in enabling drivers to perceive important information with minimal distractions when multiple messages are simultaneously provided by integrated ADASs. To this end, this study introduces three types of human-machine interfaces (HMIs) for an integrated ADAS: 1) three messages using a visual display only, 2) four messages using a visual display only, and 3) three messages using visual plus auditory displays. Meanwhile, the differences in driving performance across three HMI types are examined to investigate the impacts of information quantity and display formats on driving behaviors. Additionally, variations in drivers' responses to the three HMI types are examined. Driving behaviors of 51 drivers with respect to three HMI types are investigated in eight field testing scenarios. These scenarios include warnings for rear-end collision, lateral collision, forward collision, lane-change, and curve speed, as well as notifications for emergency events downstream, the specified speed limit, and car-following behaviors. Results indicate that, compared to a visual display only, presenting three messages through visual and auditory displays enhances driving performance in four typical scenarios. Compared to the presentation of three messages, a visual display offering four messages improves driving performance in rear-end collision warning scenarios but diminishes the performance in lane-change scenarios. Additionally, the relationship between information quantity and display formats shown on HMIs and driving performance can be moderated by drivers' gender, occupation, driving experience, annual driving distance, and safety attitudes. Findings are indicative to designers in automotive industries in developing HMIs for future CVs.

Investigating built environment and traffic flow impact on crash frequency in urban road networks.

While numerous studies have examined the factors that influence crash occurrence, there remains a gap in understanding the intricate relationship between built environment, traffic flow, and crash occurrences across different spatial units. This study explores how built environment attributes, and dynamic traffic flow characteristics affect crash frequency by focusing on proposed traffic density-based zones (TDZs). Utilizing a comprehensive dataset from Greater Melbourne, Australia, this research emphasizes on the dynamic traffic flow variables and insights from the Macroscopic Fundamental Diagram model, considering parameters such as shockwave velocity and congestion index. The association between the potential influencing factors and crash frequency is examined using a random parameter negative binomial regression model. Results indicate that the data segmentation based on TDZs is instrumental in establishing a more refined crash model compared to traditional planning-based zones, as demonstrated by improved goodness-of-fit measures. Factors including density (e.g., employment density), network design (e.g., road density and highway density), land use diversity (e.g., job-housing balance and land use mixture), and public transit accessibility (e.g., bus route density) are significantly associated with crash occurrence. Furthermore, the unobserved heterogeneity effects of the shockwave velocity and congestion index on crashes are revealed. The study highlights the significance of incorporating dynamic traffic flow variables in understanding crash frequency variations across different spatial units. These findings can inform optimal real-time traffic monitoring, environmental design, and road safety management strategies to mitigate crash risks.

An integrated data- and theory-driven crash severity model.

For crash severity modeling, researchers typically view theory-driven models and data-driven models as different or even conflicting approaches. The reason is that the machine-learning models offer good predictability but weak interpretability, while the latter has robust interpretability but moderate predictability. In order to alleviate the tension between them, this study proposes an integrated data- and theory-driven crash-severity model, known as Embedded Fusion model based on Text Vector Representations (TVR-EF), by leveraging the complementary strengths of both. The model specification consists of two parts. (i) the data-driven component not only mitigate the deficiencies of traditional econometric models, where one-hot encoding is frequently used and makes it impossible to observe semantic relatedness between variable categories, but also enhances the interpretability for the relationship between crash severity and potential influencing factors using the learned embedding weight matrix. (ii) In the theory-driven component, the multinomial logit model is implemented as a 2D-Convolutional Neural Network (2D-CNN) to increase flexibility and decrease dependency on prior knowledge for different crash-severity outcomes. A crash dataset from Guangdong Province, China, is utilized to estimate the TVR-EF model, which is then benchmarked against two traditional econometric models and three widely used machine-learning models. Results indicate that TVR-EF model does not only improve the predictive performance but also makes it easier to interpret.

Exploring the association between quantified road safety target attributes and their success: An empirical analysis from OECD countries using panel data.

INTRODUCTION: Setting quantified road safety targets has been recognized as a best practice to eliminate road fatalities by international organizations such as the Organisation for Economic Co-operation and Development (OECD). Previous studies have examined the relationship between setting quantified road safety targets and road fatality reduction. However, little attention has been paid to the association between the targets' characteristics and their successes under certain socioeconomic conditions. METHOD: This study aims to fill this gap by identifying the quantified road safety targets that are the most achievable. Specifically, using panel data on the OECD countries' quantified road safety targets, this study develops a fixed effects model to determine the specific characteristics (i.e., target duration and level of ambition) of an optimal target to make it as achievable as possible for OECD countries. RESULTS: The study finds that a significant association exists between target duration, level of ambition, and target achievement, with targets that have lower levels of ambition having higher achievements. Moreover, different groups of OECD countries carry different characteristics (e.g., target duration) that concern their most achievable targets. CONCLUSIONS: The findings suggest that, in terms of duration and level of ambition, OECD countries' target setting should establish their own socioeconomic development conditions. This provides government officials, policymakers, and practitioners with useful references for the future quantified road safety target settings that are the most likely to be achieved.

A proactive crash risk prediction framework for lane-changing behavior incorporating individual driving styles.

Driving style may have an important effect on traffic safety. Proactive crash risk prediction for lane-changing behaviors incorporating individual driving styles can help drivers make safe lane-changing decisions. However, the interaction between driving styles and lane-changing risk is still not fully understood, making it difficult for advanced driver-assistance systems (ADASs) to provide personalized lane-changing risk information services. This paper proposes a personalized risk lane-changing prediction framework that considers driving style. Several driving volatility indices based on vehicle interactive features have been proposed, and a dynamic clustering method is developed to determine the best identification time window and methods of driving style. The Light Gradient Boosting Machine (LightGBM) based on Shapley additive explanation is used to predict lane-changing risk for cautious, normal, and aggressive drivers and to analyze their risk factors. The highD trajectory dataset is used to evaluate the proposed framework. The obtained results show that i) spectral clustering and a time window of 3 s can accurately identify driving styles during the lane-changing intention process; ii) the LightGBM algorithm outperforms other machine learning methods in personalized lane-changing risk prediction; iii) aggressive drivers seek more individual driving freedom than cautious and normal drivers and tend to ignore the state of the car behind them in the target lane, with a greater lane-changing risk. The research conclusion can provide basic support for the development and application of personalized lane-changing warning systems in ADASs.

Do footbridge and underpass improve pedestrian safety? A Hong Kong case study using three-dimensional digital map of pedestrian network.

Hong Kong is a compact city with high activity and travel intensity. In the past decades, many footbridges and underpasses were installed to reduce the pedestrian-vehicle conflicts on urban roads. However, it is rare that the effects of configuration of pedestrian network on pedestrian crashes are investigated. In Hong Kong, many footbridges and underpasses are connected to major transport hubs and commercial building development and become parts of giant elevated and underground walkway systems. It is challenging to characterize such a complicated pedestrian network. In this study, a three-dimensional digital map is applied to estimate the connectivity and accessibility of pedestrian network, and measure the relationship between pedestrian network characteristics and pedestrian safety at the macroscopic level. Hence, the effects of footbridge and underpass on pedestrian safety are examined. For example, comprehensive built environment, pedestrian network, traffic, and crash data are aggregated to 379 grids (0.5 km × 0.5 km). Then, multivariate Poisson lognormal regression approach is applied to model fatal and severe injury (FSI) and slight injury pedestrian crashes, with which the effects of unobserved heterogeneity, spatial correlation, and correlation between crash counts are accounted. Results indicate that population density, traffic volume, walking trip, footpath density, node density, number of vertices per footpath segment, bus stop, metro exit, residential area, commercial area, and government and utility area are positively associated with pedestrian crashes. In contrast, average gradient, accessibility of footbridge, accessibility of underpass, and number of crossings per road segment are negatively associated with pedestrian crashes. In other word, pedestrian safety would be improved when footbridge and underpass are more accessible. Findings have implications for the design and planning of pedestrian network to promote walkability and improve pedestrian safety.

Effects of collision warning characteristics on driving behaviors and safety in connected vehicle environments.

With the emerging connected vehicle (CV) technologies, a novel in-vehicle omni-direction collision warning system (OCWS) is developed. For example, vehicles approaching from different directions can be detected, and advanced collision warnings caused by vehicles approaching from different directions can be provided. Effectiveness of OCWS in reducing crash and injury related to forward, rear-end and lateral collision is recognized. However, it is rare that the effects of collision warning characteristics including collision types and warning types on micro-level driver behaviors and safety performance is assessed. In this study, variations in drivers' responses among different collision types and between visual only and visual plus auditory warnings are examined. In addition, moderating effects by driver characteristics including drivers' demographics, years of driving experience, and annual driving distance are also considered. An in-vehicle human-machine interface (HMI) that can provide both visual and auditory warnings for forward, rear-end, and lateral collisions is installed on an instrumented vehicle. 51 drivers participate in the field tests. Performance indicators including relative speed change, time taken to accelerate/decelerate, and maximum lateral displacement are adopted to reflect drivers' responses to collision warnings. Then, generalized estimation equation (GEE) approach is applied to examine the effects of drivers' characteristics, collision type, warning type and their interaction on the driving performance. Results indicate that age, year of driving experience, collision type, and warning type can affect the driving performance. Findings should be indicative to the optimal design of in-vehicle HMI and thresholds for the activation of collision warnings that can increase the drivers' awareness to collision warnings from different directions. Also, implementation of HMI can be customized with respect to individual driver characteristics.

The impacts of non-motorized traffic enforcement cameras on red light violations of cyclists at signalized intersections.

INTRODUCTION: With a significant increase in accidents involving cyclists, more attention has been paid to cycling safety. Previous studies on traffic accident revealed that red-light violations of non-motorized vehicles have become the leading cause of crashes at signalized intersections. The objective of this study is to investigate the impact of non-motorized traffic enforcement cameras (NTECs) on the red-light running behavior of cyclists, including ordinary e-bike riders, delivery e-bike riders, and bicyclists. METHOD: An observational study of 5,217 cyclists was conducted at six primary intersections in the downtown areas of Nanjing, China. A random parameter logit model was used to explore the safety effect of the NTECs and other factors related to red-light violation behavior. RESULTS: The results indicate higher reductions in red-light violations at intersections with the NTECs compared than at the non-adjacent intersections without the NTECs. Furthermore, the NTECs demonstrated a beneficial but smaller impact on the reduction of violations at adjacent intersections. Another primary finding was that the effects of the NTECs varied among three types of cyclists (ordinary e-bike riders, delivery e-bike riders, and bicyclists). CONCLUSIONS: The NTECs were found to be most effective in the case of delivery e-bike riders, followed by ordinary e-bike riders and bicyclists. In addition, the factors associated with the red-light violation behaviors of these three groups were also found to be different. In general, group size, maximum waiting time, waiting position, and visual search were significantly related to the probability of red-light violations in all three groups. PRACTICAL APPLICATIONS: Based on these findings, this study provides some feasible suggestions for improving the effect of the NTECs and for the future extension of the NTECs installation, such as the randomization of the enforcement and publicity campaigns.

Distractions by work-related activities: The impact of ride-hailing app and radio system on male taxi drivers.

Use of ride-hailing mobile apps has surged and reshaped the taxi industry. These apps allow real-time taxi-customer matching of taxi dispatch system. However, there are also increasing concerns for driver distractions as a result of these ride-hailing systems. This study aims to investigate the effects of distractions by different ride-hailing systems on the driving performance of taxi drivers using the driving simulator experiment. In this investigation, fifty-one male taxi drivers were recruited. During the experiment, the road environment (urban street versus motorway), driving task (free-flow driving versus car-following), and distraction type (no distraction, auditory distraction by radio system, and visual-manual distraction by mobile app) were varied. Repeated measures ANOVA and random parameter generalized linear models were adopted to evaluate the distracted driving performance accounting for correlations among different observations of a same driver. Results indicate that distraction by mobile app impairs driving performance to a larger extent than traditional radio systems, in terms of the lateral control in the free-flow motorway condition and the speed control in the free-flow urban condition. In addition, for car-following task on urban street, compensatory behaviour (speed reduction) is more prevalent when distracted by mobile app while driving, compared to that of radio system. Additionally, no significant difference in subjective workload between distractions by mobile app and radio system were found. Several driver characteristics such as experience, driving records, and perception variables also influence driving performances. The findings are expected to facilitate the development of safer ride-hailing systems, as well as driver training and road safety policy.

Air travel choice, online meeting and passenger heterogeneity - An international study on travellers' preference during a pandemic.

This study empirically identifies business travellers' preferences during the COVID-19 pandemic across different regions. A stated preference study was conducted during April to June 2021 on respondents in the U.S., the city of Shanghai in mainland China and Hong Kong. Generalised mixed multinomial logit (GMXL) models are estimated incorporating attributes of travel characteristics, severity levels of the pandemic, and health control measures at the airport. When an online meeting is inapplicable, respondents from Shanghai and Hong Kong highly value heath control measures, and are not sensitive to the time spent at airport health checkpoints. In comparison, U.S. respondents are averse to the time spent for health check, the reporting of personal information, travel history, symptoms, and the requirements of compulsory mask wearing and onsite sample testing. However, when online meeting is applicable, all the respondents show no appreciation for health control measures, while the U.S. respondents are twice more averse to the time spent at airport health checkpoints. Online meeting reduces the intention of international business travel amid the pandemic for passengers in Shanghai and Hong Kong, but imposes no significant effects on U.S. travellers. Such significant heterogeneity in traveller preference partly explains the different recovery patterns observed in various aviation markets, and justifies individualized travel arrangements and service priority in fulfilling pandemic control requirements across different regions. Our study also suggests that there are commonly accepted areas for global cooperation such as the sharing of vaccination record, and the option of online meeting calls for convenient travel arrangements amid pandemic to all countries.

The effect of online meeting and health screening on business travel: A stated preference case study in Hong Kong.

This study quantifies the effects of health control measures at the airport on passenger behaviour related to business travel. A stated preference survey was conducted over potential air travellers in Hong Kong in the context of COVID-19 pandemic. Panel latent class models were estimated to understand passenger preference toward new travel requirements given the applicability of online meeting. Online meeting is applicable in cases where it is a good substitute of air travel and achieves the same outcomes of a trip, and inapplicable otherwise. Empirical results indicate that traveller subgroups are affected in different ways. When an online meeting is inapplicable, nearly 75% of the respondents prefer to travel for business and undertake health screenings. These passengers (identified as "captive" business travellers) perceive such measures necessary to lower health related risks during air travel. As such, they are willing to spend up to 21 to 38 min on the health control measures such as vaccination record requirements and test involving sample collection. When an online meeting is applicable, the share of "choice" business travellers is about 45%, among whom the attitudes towards health control measures become more averse. The average weighted willingness-to-pay for the time saved at health checkpoints increase significantly. The aviation industry thus faces a "double-hit" problem: operation costs will increase due to pandemic control measures, and the resultant inconvenience, extra time and costs further reduces travel demand. Unlike previous short pandemics, business travel is likely to suffer with an extended decline until the pandemic is fully controlled. These identified challenges call for financial and operational support to help the aviation industry reach a sustainable "new normal". The high value of time saved at check points also justifies investments that make the pandemic control and health measures efficient and smooth. Travellers' time spent on airport health control should be within 20 min to avoid substantial negative impacts on business travel demand.

Effect of urban street trees on pedestrian safety: A micro-level pedestrian casualty model using multivariate Bayesian spatial approach.

In the past decades, trees were considered roadside hazard. Street trees were removed to provide clear zone and improve roadside safety. Nowadays, street trees are considered to play an important role in urban design. Also, street tree is considered a traffic calming measure. Studies have examined the effects of urban street trees on driver perception, driving behaviour, and general road safety. However, it is rare that the relationship between urban street trees and pedestrian safety is investigated. In this study, a micro-level frequency model is established to evaluate the effects of tree density and tree canopy cover on pedestrian injuries, accounting for pedestrian crash exposure based on comprehensive pedestrian count data from a state in Australia, Melbourne. In addition, effects of road geometry, traffic characteristics, and temporal distribution are also considered. Furthermore, effects of spatial dependency and correlation between pedestrian casualty counts of different injury severity levels are accounted using a multivariate Bayesian spatial approach. Results indicate that road width, bus stop, tram station, on-street parking, and 85th percentile speed are positively associated with pedestrian casualty. In contrast, pedestrian casualty decreases when there is a pedestrian crosswalk and increases in tree density and canopy. Also, time variation in pedestrian injury risk is significant. To sum up, urban street trees should have favorable effect on pedestrian safety. Findings are indicative to optimal policy strategies that can enhance the walking environment and overall pedestrian safety. Therefore, sustainable urban and transport development can be promoted.

Exploring the impacts of street layout on the frequency of pedestrian crashes: A micro-level study.

INTRODUCTION: Pedestrian safety has become a critical issue since walking is increasingly promoted as a sustainable transport mode. However, pedestrians are vulnerable to severe injury and mortality in road crashes. Therefore, it is important to understand the factors that affect the safety of pedestrians. This paper investigates the impacts of street layout on the frequency of pedestrian crashes by examining the interactive pattern of built environment, crossing facilities, and road characteristics. METHOD: A surrogate exposure variable of pedestrian crashes at the road-segment level is proposed by considering the locations of crossing facilities, distribution of points of interest (POIs), road characteristics, and pedestrian activities. A network-based kernel density technique is used to identify the pedestrian crash risk at the road segment level. Bayesian spatial models based on different exposure variables are employed and compared. RESULTS: The results suggest that models using the surrogate exposure of pedestrian crashes provide better model fit than the ones simply using the density of pedestrians. It is also found that the presence of POIs is related to a higher risk of pedestrian-vehicle crash. In addition, a significantly higher number of pedestrian crashes are found to occur on segments with more bus stops and metro stations. Results also show that the longer the distance between the crossing facilities and road segments, the more pedestrian crashes are observed. CONCLUSIONS: The proposed aggregated indicator can provide more efficient exposure and higher prediction accuracy than the density of pedestrians. Besides, the POIs, crossing facilities, and road types were all significantly related to pedestrian crashes. PRACTICAL APPLICATIONS: Our results suggest that the locations of POIs and transport facilities should be planned in a way that can decrease the number of road crossed or guide pedestrians to take safe crossing path.

A novel traffic conflict risk measure considering the effect of vehicle weight.

INTRODUCTION: Vehicle weight is deterministic to the impact force in collision, and thus the injury risk of vehicle occupants. In China, involvement of heavy vehicles in overall and fatal crashes are prevalent, even though heavy vehicles only constitute a small proportion of overall registered motor vehicles. However, vehicle weight is rarely considered in the existing traffic conflict risk prediction and assessment models because of the unavailability of required data. METHOD: Novel risk indicators for the diagnosis of traffic conflict risk map, considering the effect of vehicle weight, are proposed, with the advantage of comprehensive traffic flow characteristics and vehicle weight data using Weigh-in-Motion (WIM) technique. Weight-incorporated risk level (WRL) and weight integrated risk level (WIRL) are established to quantify the traffic conflict risk, at an instant and over a specified time period, respectively, by extending the conventional traffic conflict risk measures including time-to-collision (TTC) and modified potential collision energy (PCE). Then, a microscopic traffic simulation model is adopted to estimate the traffic conflict risk map along a highway segment that has partial lane closure. The traffic conflict risk performances, between the risk indicators with and without considering the vehicle weight, are compared. RESULTS: The traffic conflict risks estimated using conventional risk indicators without considering the vehicle weight are generally lower than that based on WRL and WIRL. The difference is more profound when the proportion of heavy vehicles in the traffic stream increases. CONCLUSIONS: The finding is indicative to remedial engineering measures including variable message sign, speed limit, and ramp metering that can mitigate the real-time crash risks on highways, especially in adverse environmental and weather conditions, with due consideration of vehicle composition and crash worthiness of vehicles.

A resampling approach to disaggregate analysis of bus-involved crashes using panel data with excessive zeros.

Public bus constitutes more than 70% of the overall road-based public transport patronage in Hong Kong, and its crash involvement rate has been the highest among all public transport modes. Though previous studies had identified explanatory factors that affect the crash risk of buses, use of considerably imbalanced crash data with excessive zero observations could lead to inaccurate parameter estimation. This study aims to resolve the excess zero problem of disaggregate analysis of bus-involved crashes based on synthetic data using a Synthetic Minority Over-Sampling Technique for panel data (SMOTE-P). Dataset comprising crash, traffic, and road inventory data of 88 road segments in Hong Kong during the period from 2014 to 2017 is used. To assess the data balancing performance, other common data generation approaches such as Random Under-sampling of the Majority Class (RUMC) technique, Cluster-Based Under-Sampling (CBUS), and mixed resampling, are also considered. Random effect Poisson (REP) models based on synthetic data and random effect zero-inflated Poisson (REZIP) model based on original data are estimated. Results indicate that REP model based on synthetic data using SMOTE-P outperforms REZIP model based on original data and REP models based on synthetic data using RUMC, CBUS and mixed approaches, in terms of statistical fit, prediction error, and explanatory factors identified. Results of model estimation based on SMOTE-P suggest that factors including morning peak, evening peak, hourly traffic flow, average lane width, road length, bus stop density, percentage of bus in the traffic stream, and presence of bus priority lane all affect the bus-involved crash frequency. More importantly, this study provides a feasible solution for disaggregate crash analysis with imbalanced panel data.

Planning considerations of green corridors for the improvement of biodiversity resilience in suburban areas.

The world is experiencing a rapid loss in the biodiversity of pollinator insects. Habitat segmentation caused by infrastructures is one of the contributing factors. To improve the habitat connectivity of pollinator insects, it is proposed in this study to build green corridors for pollinators over linear infrastructures such as highways. In the context of suburban areas of a large city, this study examines differences in air parameters between natural environments and a roadside environment based on monitored and estimated data. Influences of different green corridor designs on floral scent dispersion are also investigated using computational fluid dynamics (CFD) modeling and simulation. It is found that, if flower plants are installed on highway overpasses, the floral scents would be better preserved as compared with those in a natural environment due to the lower concentrations of oxidative radicals in the air above highways. The stronger floral scents and their wider dispersion may help attract pollinators. Conversely, highway air contains a variety of volatine organic compounds (VOCs) that are traced to highway operations and pavements. Hence, the overall profile of VOCs in a highway environment differs from that in a natural environment. Results from CFD modeling and simulation suggest that the use of green corridors planted with flowers on the highway overpass can greatly improve the connectivity of floral scents. Hence, with proper engineering design and right combination of plant species, green corridors built on highway overpasses have the potential to facilitate pollinators to cross the road, thereby improving their habitat connectivity and resilience against declining biodiversity.

Investigating the uniqueness of crash injury severity in freeway tunnels: A comparative study in Guizhou, China.

INTRODUCTION: With the rapid development of transportation infrastructures in precipitous areas, the mileage of freeway tunnels in China has been mounting during the past decade. Provided the semi-constrained space and the monotonous driving environment of freeway tunnels, safety concerns still remain. This study aims to investigate the uniqueness of the relationships between crash severity in freeway tunnels and various contributory factors. METHOD: The information of 10,081 crashes in the entire freeway network of Guizhou Province, China in 2018 is adopted, from which a subset of 591 crashes in tunnels is extracted. To address spatial variations across various road segments, a two-level binary logistic approach is applied to model crash severity in freeway tunnels. A similar model is also established for crash severity on general freeways as a benchmark. RESULTS: The uniqueness of crash severity in tunnels mainly includes three aspects: (a) the road-segment-level effects are quantifiable with the environmental factors for crash severity in tunnels, but only exist in the random effects for general freeways; (b) tunnel has a significantly higher propensity to cause severe injury in a crash than other locations of a freeway; and (c) different influential factors and levels of contributions are found to crash severity in tunnels compared with on general freeways. Factors including speed limit, tunnel length, truck involvement, rear-end crash, rainy and foggy weather and sequential crash have positive contributions to crash severity in freeway tunnels. Practical applications: Policy implications for traffic control and management are advised to improve traffic safety level in freeway tunnels.

The trade-off between safety and time in the red light running behaviors of pedestrians: A random regret minimization approach.

Pedestrian safety has been a major concern in Hong Kong, where walking is an important access mean to urban transportation services and pedestrian-vehicle conflicts are prevalent. Red light running violation of pedestrians is a leading cause of pedestrian-vehicle crashes at the signal intersections. It is necessary to examine the possible factors including personal characteristics and road environments that affect the propensities of red light running violation of pedestrians. Therefore, effective traffic control and enforcement measures can be implemented to deter against the red light running behaviors of pedestrians. This study attempts to examine the roles of trade-off between safety and time, as well as situational features and personality traits, in the red light running behaviors of pedestrians using a stated preference survey method. Then, a regret-based panel mixed multinomial logit model is established for the association measure between propensities of red light running violation and possible factors, with which the effects of unobserved heterogeneity and correlation in the choices between different scenarios of the same person are considered. Results indicate that the choice decision of pedestrians are more sensitive to a reduction in time loss, as compared to the equivalent increase in safety risk. In addition, the safety versus time trade-off may vary between pedestrian groups. Furthermore, presence and type of another violator also significantly affect the propensities of red light running violation. Such findings are indicative to effective policy interventions that can deter against the red light running behaviors of vulnerable pedestrian groups. Therefore, overall pedestrian safety level can be improved in the long term.

Propensity score methods for road safety evaluation: Practical suggestions from a simulation study.

The propensity score (PS) based method has been increasingly used in road safety evaluation studies. However, several major considerations regarding its implementation arise when using the PS method. First, as is well known, the PS method is 'data hungry' in terms of the number of treated and control units, however, it is sometimes difficult and time-consuming to construct a large sample in road safety studies. It would be helpful to better understand how to choose a proper sample size, as well as the ratio of the number of treated units to the control ones. Second, the criteria used for covariates selection of the PS model were not fully consistent across the existing road safety evaluation studies. Due to the complicated mechanisms behind the implementation of road safety measures and policies, including all relevant covariates that affect both the selection into treatment (i.e., implementation of road safety measures) and the outcomes (i.e., road accidents) is impossible. In this paper, we conduct a simulation study to investigate such issues and provide some practical suggestions for using PS methods in road safety evaluations. The estimator considered in this study is the inverse probability weighting estimator based on the PS. Our results suggest that the bias and variance of the estimated treatment effect will remain stable when the sample size reaches a certain level. A proper sample size is the one that ensures relevant covariates achieve acceptable balance. Regarding the issue of covariates selection, including the covariates that significantly affect the road accidents is recommended, regardless of whether they affect the implementation of road safety measures. This study also proposes practical procedures for using the weighting approach to evaluate the effects of road safety treatments.