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There is relatively little research dedicated to the evaluation of different temporal treatments on modelling performance. This study proposed two new methods which combined the strengths of linear...

Most of the studies are focused on the general crashes or total crash counts with considerably less research dedicated to different crash types. This study employs the Systemic approach for detection of hotspots and comprehensively cross-validates five multivariate models of crash type-based HSID methods which incorporate spatial and temporal random effects. It is anticipated that comparison of the crash estimation results of the five models would identify the impact of varied random effects on the HSID. The data over a ten year time period (2003–2012) were selected for analysis of a total 137 intersections in the City of Corona, California. The crash types collected in this study include: Rear-end, Head-on, Side-swipe, Broad-side, Hit object, and Others. Statistically significant correlations among crash outcomes for the heterogeneity error term were observed which clearly demonstrated their multivariate nature. Additionally, the spatial random effects revealed the correlations among neighboring intersections across crash types. Five cross-validation criteria which contains, Residual Sum of Squares, Kappa, Mean Absolute Deviation, Method Consistency Test, and Total Rank Difference, were applied to assess the performance of the five HSID methods at crash estimation. In terms of accumulated results which combined all crash types, the model with spatial random effects consistently outperformed the other competing models with a significant margin. However, the inclusion of spatial random effect in temporal models fell short of attaining the expected results. The overall observation from the model fitness and validation results failed to highlight any correlation among better model fitness and superior crash estimation.

As more attention is focused on bicycles as a mode of transportation, there is a strong need to understand microscopic behaviors of bicyclists within urban traffic systems. To respond to th...

Objective: Most of the extensive research dedicated to identifying the influential factors of hit-and-run (HR) crashes has utilized the typical Maximum Likelihood Estimation Binary Logit models, and none of them have employed the real-time traffic data. To fill this gap, this study focused on investigating contributing factors of HR crashes, as well as the severity levels of HR. Methods: This study analyzed four-year crash and real time loop detector data by employing the hierarchical Bayesian models with random effects within a sequential Logit structure. Along with the evaluation of impact of random effects on model fitness and complexity, the prediction capability of the models was also examined. Stepwise incremental sensitivity and specificity were calculated and ROC (Receiver Operating Characteristic) curve was utilized to graphically illustrate the predictive performance of the model. Results: Among the real-time flow variables, the average occupancy and speed from upstream detector was observed to be positively correlated with HR crash possibility. The average upstream speed and speed difference of upstream and downstream speed were correlated with the occurrence of severe HR crashes. Apart from real-time factors, the other variables found influential for HR and severe HR crashes were length of segment, adverse weather conditions, dark lighting conditions with malfunctioning street light, driving under influence of alcohol, width of inner shoulder, and night time. Conclusions: This study suggests the potential traffic conditions of HR and severe HR occurrence, which refer to relatively congested upstream traffic conditions with high upstream speed and significant speed deviations on long segments. The above findings suggest that traffic enforcement should be directed towards mitigating the risky driving under the aforementioned traffic conditions. Moreover, the enforcement agencies may employ alcohol checkpoints to counter DUI during the night time. As per the engineering improvements, wider inner shoulders may be constructed to potentially reduce HR cases and the street lights should be installed and maintained in working conditions to make the roads less prone to such crashes.

This paper deals with the timetable rescheduling problem based on the feedback of train-set's circulation in high speed railway transportation. Given the original timetable and the corresponding train-set circulation scheme, the authors describe an integer linear programming model based on the fixed departure/arrival order, which can tackle a static timetable rescheduling to optimize the train-set circulation, bring out more connections, especially without direction change in through station between train lines. Lingo11.0 is used in the case study.

Planning on utilization of train-set is one of the key tasks of transport organization for passenger dedicated railway in China. It also has strong relationships with timetable scheduling and operation plans at a station. To execute such a task in a railway hub pooling multiple railway lines, the characteristics of multiple routing for train-set is discussed in term of semicircle of train-sets' turnover. In programming the described problem, the minimum dwell time is selected as the objectives with special derive constraints of the train-set's dispatch, the connecting conditions, the principle of uniqueness for train-sets, and the first plus for connection in the same direction based on time tolerance σ. A compact connection algorithm based on time tolerance is then designed. The feasibility of the model and the algorithm is proved by the case study. The result indicates that the circulation model and algorithm about multiple routing can deal with the connections between the train-sets of multiple directions, and reduce the train's pulling in or leaving impact on the station's throat.

Scientific and accurate transportation volume prediction for freight corridor is one of the essential works of transport network planning, which masters the corridor's development, trend, characteristic, raw and quantity. The traditional prediction method of freight volume includes exponential smoothing model, regression Prediction, GM (1, 1) model and combined model. This paper explores the procedures and characteristics of these models. And the GM (1,1) model and cubic exponential smoothing model are used to predict the Shuozhou-Huanghua railway freight volume. Based on that, the suitable conditions of the traditional models are discussed as well.