Journal of Transportation Engineering, Part A: Systems

The transportation decision-making process is complicated in that it often involves conflicting interests and expectations of different stakeholders. To ensure achieving sound decisions, various performance criteria in economic, social, and environmental dimensions are typically utilized to assess and prioritize investment alternatives. This paper introduces an entropy-STEP (e-STEP, where STEP is the uppercase of the word step) multiobjective trade-off analysis method for optimal transportation investment decisions. Its entropy component derives normalized relative importance for multiple noncommensurable performance criteria by refining the prior relative importance with additional context-dependent information on the relative importance. The STEP component conducts trade-off analysis and makes efficient investment decisions using a multiobjective optimization formulation. The model is converted to a linear programming model with refined relative importance for performance criteria to derive efficient solutions. The proposed method is applied in a computational study using data on a state-level toll highway capital investment program in the United States. The same data set is used to execute the traditional STEP method and surrogate worth trade-off (SWT) method used in the current practice. Cross comparisons of decision outcomes reveal that the e-STEP method outperforms the other two methods, suggesting that it could be adopted by transportation agencies to develop capital investment programs.

Previous studies have examined the relationships between run-off-road (ROR) crashes and the contributing factors; however, the impact of lighting conditions has been insufficiently addressed. As a result, the objective of this study was to research the effect of lighting conditions on the injury severity of ROR crashes that involve large trucks. Based on the crash data pertaining to large trucks in the state of Oregon from 2007 to 2013, two separate mixed logit models were developed to capture the contributing factors that affect injury severity in each lighting condition. The levels of injury severity sustained by truck drivers were categorized into three main categories: severe injury (fatal and incapacitating), minor injury (nonincapacitating and possible injury), and no injury. The mixed logit model was used to account for unobserved factors (i.e., unobserved heterogeneity). Estimation results indicated that there are significant differences between dark and lighted conditions, that the level of injury severity outcomes was highly influenced by several complex interactions between factors, and that the effects of some factors could vary across observations. The contributing factors include driver, traffic flow, roadway geometric features, land use, and time characteristics.

The 2010 highway capacity manual (HCM) provides an approach to estimate the mutual between pedestrians and automobiles at signalized intersections. However, the manual provides little guidance for capturing the interactions between pedestrians and automobiles at unsignalized midblock pedestrian crosswalks on urban street segments. The movements of vehicles on urban streets can be significantly impacted by midblock delays and stops. One of the performance measures in evaluating automobile performance on urban street segments is the travel speed. The manual computes the segment travel speed based on the segment length, the segment running time, and the control delay. Friction conditions, such as the midblock pedestrian crossings, interfere with the traffic flow on urban street segments and therefore increase the segment running time, which consequently lowers the travel speed and the level of service (LOS) of the segment. The frequency of the interference to vehicles is related to the frequency of the arrival of pedestrians and the flow of vehicles. This paper develops and evaluates a model to estimate the frequency of midblock pedestrian interference to vehicles at unsignalized midblock crosswalks on urban street segments, using the traffic volume, the pedestrian volume, and the median type and posted speed limit as the independent variables. The results show that the traffic volume and the pedestrian volume are the most significant models.

This paper is aimed at developing an optimization framework for the concurrent calibration of demand and supply parameters in a dynamic traffic assignment (DTA) model. The proposed approach calibrates route choice, along with drivers’ behavioral parameters, and estimates origin-destination (OD) flows in a large-scale network in a Paramics microscopic traffic simulation model. A mathematical formulation is defined to quantify the reliability of the observations. A genetic algorithm (GA) is selected as a suitable solution algorithm for the resulting nonlinear stochastic optimization problem. The application of the proposed methodology is implemented in the large-scale network in the business district core of downtown Toronto, Ontario, Canada. For this network, the emerging traffic surveillance data from in-vehicle navigation system technology provide an enriched source of disaggregated speed data. The empirical results from various experiments support the hypothesis that incorporating in-vehicle navigation system speed data can improve the calibration accuracy and minimize the reliance of the calibration process on a priori OD flows. The quality of the solution and convergence speed of a GA is further enhanced by dividing the GA population into multiple demes and running the GA on a high-performance computing cluster (HPCC) with multiple processors (i.e., parallel distributed GA, PDGA). In addition, this research takes a further step toward analyzing the temporal variations of the driving behavior of travelers. The case study establishes an example for modelers and practitioners who are interested in calibrating a large-scale traffic simulation model. The developed simulation model for traffic has the potential to serve as a test bed on a HPCC for more efficient computation and integration with other optimization tools such as GAs.

An increasing number of smartphone apps with driving safety diagnosis functions have been developed in the market; however, empirical evidence on whether these apps are actually effective in promoting road safety or not is limited. This study examines how the use of a global positioning system (GPS)-enabled smartphone app with driving safety related diagnosis affects changes in driving behaviors on expressways. The app was developed by the authors as a simplified diagnosis tool using only GPS information. The app can measure three types of driving safety related indicators second by second, i.e., speed limit compliance, acceleration/deceleration, and driving stability, in addition to a set of subjective driving safety related indicators. This study used 187,549 epochs ($2\mathrm{s}/\mathrm{epoch}$) as the analysis unit, collected from 15 drivers who made 201 trips during a three-month driving experiment on expressways in the western part of Japan in 2014. To account for the influence of drivers’ unobserved heterogeneity in driving speed control behavior, this study built a multilevel ordered probit model. Model estimation results confirmed that, during a three-month period, using the app contributes to less dangerous driving as measured by compliance level with the speed limit; however, its effects on acceleration/deceleration and driving stability were limited. The effects of some additional functions of the app on improvement in driving safety related performance were mixed. Finally, it was revealed that drivers’ heterogeneous driving propensity is significantly influential to actual driving safety related performance.