Journal of Transportation Engineering, Part A: Systems

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 these needs, a new approach to simulate bicyclists’ riding behaviors on bike paths has been developed. This approach uses the concept of reactive and perceptive ranges to depict the behaviors of bicycle flows. A series of riding strategies widely adopted by bike riders is proposed. Using these strategies or rules, this study applies a continuous psychological-physiological force (PPF) model to simulate the bicycle riding patterns and the reactive and perceptive interactions of bicyclists. A set of controlled experiments and field observations is carried out to calibrate the simulated interactions derived from the PPF model. After validation, the PPF model is used further to produce simulated trajectories of bicyclists, and a fundamental diagram is developed. The fundamental diagram is consistent with that in field investigations and previous research reports. A sensitivity analysis is also carried out based on the simulated trajectories of bicyclists (going through a series of bike paths with different widths). The analysis demonstrates that the width of a bike path directly impacts the capacity of the path. With the increasing width of a bike path, the capacity (per unit width of the bike path) decreases. This is a result of the psychological and physiological interactions of bicyclists in response to bike paths with different widths. This result provides a good insight for the design of bike paths in the future.

Transportation agencies seek comprehensive policies and planning to overcome urban congestion and manage transportation demand with due cognizance of traveler behavior. This research develops a travel behavioral model for work-trip mode using revealed and stated choice data collected through a survey questionnaire in the city of Rawalpindi, Pakistan. The multinomial logit model specification is found best suited to develop a modal-split model and estimate travelers’ perceived expectation utility functions. The model is used to calculate elasticities and demand response to the policies of improvement in transit/bus rapid transit (BRT) and implement congestion pricing on major arterials of an urban road network. Travel demand is found elastic with respect to congestion pricing and out-of-vehicle travel time. It is concluded that improvement in transit services by introducing BRT alone does not induce a major change in the share proportion of automobile demand; however, congestion pricing has a significant effect on the reduction of automobile demand. Furthermore, the combination of two policies induces more modal-split than does congestion pricing alone. This research highlights traffic congestion pricing as one of the means of traffic demand management by demonstrating its contribution to improving urban traffic congestion.

Because of the expected shift from ordinary to autonomous vehicles, the role of public transport (PT) will have to be enhanced, and thus there is a need to pay more attention to its design procedures and evaluation processes. This study compares previous models and methodologies by creating a quality-evaluation platform that enables a comparison of their solution qualities. The evaluation framework developed consists of an integration component between the criteria and objectives of the passengers, the agencies, and the authorities. A set of performance indicators is selected from the literature to build an evaluation rating system. Existing benchmark networks are adopted in the numerical experiments, and interesting findings are obtained. The evaluation results reveal that the quality of the solutions declines with an increase in the network size, and for large networks, the consideration of an unlimited capacity of transport modes is too simplified; this points to the need for a layer-by-layer analysis. In addition, this study shows the level of importance in making transfers. The methodology developed can help planners to improve and design better PT networks.

In this paper, we present a multiscale approach to estimate the expected number of traffic crashes, (referred to here as expected crashes) from observed crash frequency data using the unnormalized Haar wavelet transform. The Haar transform decomposes crash count data into sums (scaling coefficients) and differences (wavelet coefficients) of Poisson distributed crash counts, which in turn are Poisson- and Skellam-distributed, respectively. This process compresses the expected crashes information into a few large wavelet detail coefficients; the random fluctuations of crash counts are relatively smaller and (evenly) spread over all wavelet detail coefficients. This allows us to effectively suppress the crashes’ random fluctuations by shrinking or thresholding the wavelet detail coefficients (i.e., setting their values below a specific threshold to zero). The appropriate amount of shrinking or thresholding is determined using Poisson’s unbiased risk estimate (PURE), which essentially minimizes the mean square error risk between the estimated expected crashes and the true unknown expected crashes. The approach can also be viewed from the point of view of nonparametric spatial clustering and smoothing, where suppression of wavelet coefficients results in smoothing of crash counts. However, in the proposed approach, the amount of smoothing is adaptive to the features at the different locations and scales. In areas where expected crashes are relatively uniform, the approach performs significant smoothing; in contrast, little smoothing is performed in areas where expected crashes vary significantly. We illustrate the method on simulated Poisson data as well as observed crash counts on an Interstate stretch in Virginia and compare it with the more traditional empirical Bayes approach. Results show good performance of the proposed approach.

This paper presents an agent-based modeling and simulation (ABMS) framework to evaluate the safety performance impacts of connected vehicle (CV) technologies in a work zone setting. This research is primarily motivated by the lack of a systematic evaluation platform to verify the safety benefits of CV technology in diverse settings, such as a work zone, caused by either a lane drop, speed reduction, or both. The guiding research question is how and when the safety benefits will start to appear as the market penetration (MP) level of CV increases and how the safety performance is affected with regard to free-flow, medium, and highly congested traffic conditions. Results show that the safety improvements are dependent on MP level and traffic flow rates. The higher the traffic flow rate, the higher MP level is needed to show improvement in the safety performance of the work zone section. In addition, a potential path of Vision Zero is likely to be achieved by an MP level of 100%. These results will inform the federal and state agencies’ decision makers to strategize the implementation of CV technologies and quantify the delicate balance between costs and benefits.

Maintenance planning for busy railway systems is challenging because there is growing pressure on increasing operation time, which reduces the infrastructure-accessible time for maintenance. This paper proposes an optimization model that is aimed at finding the best maintenance schedule for multiple components in a railway track to minimize the total cost in the planning horizon. One distinct and practical feature of the model is that the track accessible time for maintenance is limited. We formulate all relevant costs in the component’s life cycle, including maintenance cost, fixed track-closure (possession) cost, social-economic cost related to the effects of maintenance time on the train operation, and service-life shortening cost due to the shifting of activities. Generally, it is beneficial to cluster and maintain several components in a single possession because this helps reduce the cost by occupying the track only once. However, the decision must depend on the available possession time. A sensitivity analysis is performed to highlight the effects of available possession time on the number of required possessions as well as the total cost incurred.

The phenomenon of platoon dispersion deals with the spreading out of groups of vehicles discharged together from a signal (platoon) as they move along the roadway during normal traffic operations. Understanding and analyzing this behavior is important in efficient traffic operations and management. There are different platoon dispersion models reported in the literature, out of which Robertson’s model is one of the oldest and widely used. However, all the existing studies were from homogeneous and lane-based traffic conditions and few studies studied platoon dispersion behavior under traffic conditions such as those existing in India. In this study, data were collected in a typical Indian urban arterial road. To account for the heterogeneous and laneless Indian traffic, Roberston’s model was modified and an optimization approach was used to obtain the coefficients. The proposed model’s performance was evaluated and compared with the original Robertson’s model after calibrating for the specific traffic conditions under consideration. Platoon parameters were also estimated using both proposed and calibrated models and the results were compared, which showed a better performance of the proposed model compared to the calibrated Robertson’s model. Though the proposed model was tested for heterogeneous and laneless traffic data, it is in no way constraining and is generic enough to be applied for other traffic conditions.

Economists have long urged governments to use Pigouvian taxes to efficiently reduce emissions. This has rarely happened, mostly because until now, technology has not existed to precisely measure in-use emissions by location and time. In recent years, increasingly sophisticated on-board diagnostic (OBD) devices have been required for cars and trucks to monitor engine operation and measure the in-use fuel consumption and emissions of vehicles. This paper proposes the use of Pigouvian pricing to reduce emissions from on-road heavy-duty vehicles (HDVs) by utilizing emission data from OBD devices and location data from global positioning system (GPS) devices. Thus, emissions can be measured and managed over time and space using geofencing. The authors address the feasibility of using OBD and GPS devices in this way, taking into account a monitoring system consisting of OBD and GPS, designated area, pricing scheme, and the relationship with other related policies. The authors conclude that emission data can be collected reliably using OBD technology, and that geofenced time-specific pricing policies are technically feasible.

This study evaluates routes followed by residents of the Minneapolis–St. Paul metropolitan area, as measured by the Global Positioning System (GPS) component of the 2010/11 Twin Cities Travel Behavior Inventory (TBI). It finds that most commuters used paths longer than the shortest path. This is in part a function of trip distance (+, longer distance trips deviate more), trip circuity (−, more circuitous trips deviate less), number of turns (+, trips with more turns per kilometer deviate more), age of driver (−, older drivers deviate less), employment status (+, part-time workers deviate more), flexibility in work hours (+, more flexibility deviate more), and household income (−, higher-income travelers deviate less). Some reasons for these findings are conjectured.