Estimating real-time crash risk at signalized intersections: a Bayesian Generalized Extreme Value approach

The current reactive road safety assessment cannot assess real-time crash risk at signalized intersections, and as such, the real-time risk mitigation strategies could not be developed. This shortcoming is mainly attributed to a lack of a proper methodological framework that could reveal insights into the real-time crash risk using the crash signatures or precursor of crashes, otherwise known as traffic conflicts, near misses, or surrogate safety measures. This study proposes a traffic conflict-based crash estimation technique to estimate real-time crash risk at signalized intersections. In particular, a Bayesian modeling framework is employed to estimate crash risk in real-time from traffic conflicts identified by modified time-to-collision (MTTC). A Block Maxima approach corresponding to generalized extreme value distribution is used to identify traffic extremes at a micro (i.e., traffic signal cycle) level. Then, the traffic flow at the signal cycle level is included as a covariate in the model to explain time-varying crash risk across different cycles. The unobserved heterogeneity associated with the crash risk of different cycles is also addressed within the Bayesian framework. The proposed framework is tested using a total of 96 h of traffic movement video data from a signalized intersection in Queensland, Australia. A comparison between the estimated crashes and the historical crash records demonstrates the suitability of the developed model for crash risk estimations. The crash risk of each signal cycle is identified by generating a different generalized extreme value distribution for each traffic signal cycle. Further statistical analyses reveal that crash risk varies within the different periods of a typical day, with higher crash risks found during the morning and evening peak periods. This study concludes the efficacy of the proposed real-time framework in estimating the rear-end crash risk at the micro-level, allowing proactive safety management and the development of risk mitigation strategies

Logistic regression analysis: Predicting handheld conversations and texting/browsing engagement.

<p>Logistic regression analysis: Predicting handheld conversations and texting/browsing engagement.</p

Mobile phone use on a typical day and frequency.

<p>Mobile phone use on a typical day and frequency.</p

Self-reported attitudes and task-management strategies for mobile phone distracted driving.

<p>Self-reported attitudes and task-management strategies for mobile phone distracted driving.</p

Perceived crash risk per mobile phone sub-task.

<p>Perceived crash risk per mobile phone sub-task.</p

The simulated marginal utility of one-way cost for 100 randomly selected individuals.

<p>The simulated marginal utility of one-way cost for 100 randomly selected individuals.</p

Distribution of the random parameters.

<p>Distribution of the random parameters.</p

User satisfaction with train fares: A comparative analysis in five Australian cities

<div><p>In the public transport industry, travellers’ perceived satisfaction is a key element in understanding their evaluation of, and loyalty to ridership. Despite its notable importance, studies of customer satisfaction are under-represented in the literature, and most previous studies are based on survey data collected from a single city only. This does not allow a comparison across different transport systems. To address this underrepresentation, this paper reports on a study of train passengers’ satisfaction with the fare paid for their most recent home-based train trip in five Australian capital cities: Sydney, Melbourne, Brisbane, Adelaide, and Perth. Two data sources are used: a nation-wide survey, and objective information on the train fare structure in each of the targeted cities. In particular, satisfaction with train fares is modelled as a function of socio-economic factors and train trip characteristics, using a random parameters ordered Logit model that accounts for unobserved heterogeneity in the population. Results indicate that gender, city of origin, transport mode from home to the train station, eligibility for either student or senior concession fare, one-way cost, and waiting time as well as five diverse interaction variables between city of origin and socio-economic factors are the key determinants of passenger satisfaction with train fares. In particular, this study reveals that female respondents tend to be less satisfied with their train fare than their male counterparts. Interestingly, respondents who take the bus to the train station tend to feel more satisfied with their fare compared with the rest of the respondents. In addition, notable heterogeneity is detected across respondents’ perceived satisfaction with train fare, specifically with regard to the one-way cost and the waiting time incurred. An intercity comparison reveals that a city’s train fare structure also affects a traveller’s perceived satisfaction with their train fare. The findings of this research are significant for both policy makers and transport operators, allowing them to understand traveller behaviours, and to subsequently formulate effective transit policies.</p></div

The simulated marginal utility of waiting time for 100 randomly selected individuals.

<p>The simulated marginal utility of waiting time for 100 randomly selected individuals.</p

Socio-economic profiles of respondents from each city.

<p>Socio-economic profiles of respondents from each city.</p