Transportmetrica 

About: Transportmetrica is an academic journal published by Taylor & Francis. The journal publishes majorly in the area(s): Computer science & Engineering. It has an ISSN identifier of 2324-9935. Over the lifetime, 78 publications have been published receiving 154 citations.

A Lagrangian relaxation approach for the electric bus charging scheduling optimisation problem

Abstract: ABSTRACT The planning and operational decision-making problems of electric transit systems have received significant attention recently in the process of transport electrification. Given an electrified electric transit system with constructed charging facilities, a coordinated bus charging scheduling strategy can improve the system's operating efficiency by fully utilising available charging resources. This paper proposes a novel optimisation approach for the electric bus charging scheduling problem. To tackle the nonlinear relationship between the amount of energy and the time spent charging, this paper discretizes the decision variables for the charging schedule into time intervals. A linear integer program is formulated with the objective of minimising the system's total charging time. A Lagrangian relaxation-based solution approach is proposed to decompose the model into subproblems with respect to individual vehicles. The results provide a number of insights that can help transit operators design cost-effective electric transit operational plans.

A novel model to jointly estimate delay and arrival patterns by using high-resolution signal and detection data

Abstract: Delay is one of the most important traffic signal performance measures. In coordinated networks, understanding the characteristics of vehicle arrivals is important for coordination purposes and to properly estimate delays. When observed on a cyclical basis in real-time, distinctive arrival patterns can lead to similar delays, which may go undetected by contemporary delay models. This study proposes a set of enhancements to the Incremental Queue Accumulation (IQA) delay model to overcome the limitations of current models. Additionally, this study proposes a hybrid signal performance measure that combines delay and arrival patterns to depict signal performance truthfully. The enhancements to IQA are realised through an algorithm for the identification of distinctive vehicle arrival groups based on high-resolution signal and detection data. The results demonstrate that the proposed model provides reliable delay estimates (MAPE score in range 4.3–11.2%) while reporting a number of traffic arrival characteristics that are not available from the benchmarked models.

Understanding patterns of moped and seated motor scooter (50 cc or less) involved fatal crashes using cluster correspondence analysis

Abstract: ABSTRACT Moped and seated motor scooter (50 ccs or less) riders have a relatively high risk of becoming crash casualties. Comparison between 2015 and 2019 fatal crash data indicates that fatal moped crashes have increased by 76%, whereas fatal motorcycle crashes have decreased by 2%. This study collected moped and seated motor scooter-related fatal crash data for five years (2015–2019) from the Fatality Analysis Reporting System (FARS) to perform the analysis. Using an innovative categorical data analysis method known as cluster correspondence analysis (CCA), this study identified some critical clusters with a group of co-occurring variable categories. The contextual understanding of fatal crash patterns could guide authorities in developing data-driven interventions and countermeasures aiming to minimize moped collisions and related fatalities. The findings of this study can provide a better understanding of the patterns of contributing factors in moped and seated motor scooter fatal crashes.

Evaluating alternative variations of Negative Binomial–Lindley distribution for modelling crash data

Abstract: Several studies have reported the superior performance of the Negative Binomial–Lindley (NB-L) compared to the commonly used Negative Binomial distribution. Consequently, different parameterisations of the NB-L distribution have been introduced to further improve crash data modelling. However, little is known on how these models perform for different data domains. This study is documenting a comparative analysis among previously developed and two newly proposed parameterisations of the NB-L distribution, the negative binomial weighted Lindley (NB-WLindley) and the negative binomial quasi-Lindley (NB-QL). The results show that the NB-WLindley distribution performed better for the majority of data domains. Also, its generalised linear model (NB-WLindley GLM) showed superior statistical performance relative to the NB GLM and NB-L GLM. The results of this study contribute to the advancement of current predictive models used in transportation safety and provide insights for safety analysts and researchers when these models should be used.

Two-lane two-way overtaking decision model with driving style awareness based on a game-theoretic framework

Abstract: Overtaking on two-lane two-way (TLTW) highways is often associated with a high risk of crashing. However, existing models of TLTW overtaking decision, either mechanism- or learning-based, cannot handle well the dynamic coupling among the interacting drivers. For accurate overtaking modelling, it is crucial to consider the uncertainties of interacting vehicle behaviours, especially their driving styles. To address these needs, we propose a TLTW overtaking decision model using a level-k game theoretic framework, which can consider the mutual influences between the ego and oncoming vehicles of different driving styles. A dataset is built based on the TLTW overtaking experiments with two instrumented vehicles, then PCA and k-means clustering are used to classify three driving styles, i.e. aggressive, normal and conservative. By comparing the model predictions with the experiment data, the statistics and case studies show that the proposed model with driving style awareness can accurately describe driver decisions in TLTW overtaking.