Journal of Transportation Engineering, Part A: Systems

The latest edition of the Highway Capacity Manual (HCM-6) includes, for the first time, a methodology for estimating and predicting the average travel time distribution (TTD) of urban streets. Travel time reliability (TTR) metrics can then be estimated from the TTD. The HCM-6 explicitly considers five key sources of travel time variability. A literature search showed no evidence that the HCM-6 TTR model has ever been calibrated with empirical travel time data. More importantly, previous research showed that the HCM-6 underestimated the empirical TTD variability by 70% on a testbed in Lincoln, Nebraska. In other words, the HCM-6 TTR metrics reflected a more reliable roadway than would be supported by field measurements. This paper proposes a methodology for calibrating the HCM-6 TTR model so that it better estimates the empirical TTD. This calibration approach was used on an arterial roadway in Lincoln, Nebraska, and no statistically significant differences were found between the calibrated HCM-6 TTD and the empirical TTD at the 5% significance level.

Traditional customized buses travel on fixed routes, which cannot satisfy passengers’ flexibility and convenience requirements. This paper studies a demand-responsive transit (DRT) service that can continuously adjust the path based on passengers’ dynamic demand. The path optimization model is established with more realistic constraints to create a bus travel plan within a specified area, and the model not only considers the preferred time windows of passengers but also maximizes the benefits of the system. Based on simulated annealing, a dynamic genetic algorithm is designed to generate the static initial travel path, and the dynamic travel path is continuously updated to satisfy the real-time demand. To evaluate the proposed model and algorithm, a case study in a typical residential community of Beijing, China, is conducted based on transit smart card records. According to the case study results, the convenience, travel time, and economic and environmental benefits of the DRT service are assessed via comparison with traditional buses and private cars. The analysis results demonstrate the feasibility and significance of the method, and it can be used by transit planners to design a superior DRT service.

Toll plazas are one kind of highway node that pose a challenge to the flow of autonomous vehicles (AVs). Some toll roads have open road tolling (ORT), which makes it easier and more efficient for AVs, but most toll plazas do not have ORT. The authors developed a decision model to assist AVs in choosing the right lane that involves two main indicators, the rod speed ($S{R}_i$) and the decision-making value ($D_{S{R}_i}$). The decision model was applied to a six-lane toll plaza with manual toll collection (MTC), electronic toll collection (ETC), and electronic toll collection for AVs toll types. The suitable lane configurations for the varying percentages of AVs in traffic were as follows: at 10% AV, the optimal lane configuration was 4 MTC and $2\mathrm{ETC}/\mathrm{AV}$ (4M2EA), at 30% AV it was 3M3EA, at 40%–60% AV it was 3M2EA1A, and at 70% AV it was 2M2EA2A. Thus AVs should share toll lanes with ETC manned vehicles before the population of AVs reaches 30%; after that dedicated lanes may be introduced. It was also concluded that a 10% increase in AVs reduces delays by 9% if the populations of ETC and MTC vehicles are equal.

This study aims to develop a framework that integrates the technology acceptance model (TAM) into a route-switching model of travelers’ response to variable message signs (VMS). In addition to the parsimonious TAM constructs (perceived usefulness, perceived ease of use, and behavioral intention), the model is extended with latent variables that are specific to travelers’ response to VMS (perception of information quality, attitude towards route diversion, and familiarity with the network). The model also takes into account the causal relationships among the latent variables. The obtained framework is then incorporated into a route-switching model to form a hierarchical integrated choice and latent variable model. The model is tested with stated preference data from 339 Chinese road users. The results show that all six latent variables have direct and/or indirect effects on route-switching behavior according to the hypothesized relationships. Route choice attributes and individual characteristics also affect travelers’ decisions. The theoretical and practical implications of the findings are also discussed.

Assessing the railway capacity of a rail network wherein a few types of trains operate with various halting patterns is very difficult, because the headway greatly varies depending on the order in which trains of different types and with different halting patterns run. This paper proposes a new headway-based analytical capacity model that considers heterogeneous train traffic. This model is based on the equalized train concept, which is a virtual train type considering the weighted performances of all train types. The equalized train allows us to ignore the heterogeneity caused by different train types and easily calculate the average headway. Additionally, the equalized train coefficient was developed to serve as a measure of traffic heterogeneity. Monte Carlo simulation was used to approximate the station headway depending on the sequence of trains with different halting patterns. The results obtained by a case study on a major corridor in the Korean railway network reveal that, compared with previous models, the proposed method can obtain a more consistent capacity in accordance with the simulation.

The gap acceptance theory, which is widely used for capacity analysis of roundabouts, assumes absolute priority to circulating vehicles over entry vehicles. Priority violations such as priority reversal and limited priority are common in heterogeneous traffic conditions and render the gap acceptance models ineffective. The frequency of priority violations is significant for quantitative analysis. The frequency of priority violations depends on roundabout geometry, traffic heterogeneity, circulating flow, and gap parameters. Analysis of the trends of data collected for 21 roundabout entries at six roundabouts in India indicated that frequency of priority violations vary based on traffic composition and circulating flow. Separate models are formulated to estimate frequencies of priority reversal and limited priority. The effect of buses on priority reversal is found to be twice when compared to that of two-wheelers, but only half as that of heavy vehicles. The Indian Highway Capacity Manual (Indo-HCM) model for roundabout capacity was modified by incorporating priority violations, and estimates of the modified model are comparable to the observed entry flow values. The influence of frequency of limited priority on roundabout capacity is found to be six times that of frequency of priority reversal.

Mega elliptical roundabouts are considered a new kind of intersection and can be found on rural multilane highways. Their form is that of an elongated ellipse. They combine the best advantages of an unconventional median U-turn intersection and roundabout. The purpose of this research is analyzing safety performance for mega elliptical roundabout interchange using the VISSIM software version 4.30 and the surrogate safety assessment model (SSAM) software version 3. This research also considered eight other interchange designs to compare them with the mega elliptical roundabout interchange. In total, 20 scenarios were analyzed using VISSIM version 4.30 then tested by SSAM software version 3. To compare the different interchanges in terms of the mean values of conflicts, the one-way analyses of variance (ANOVA) were conducted. Based on the research results, a mega elliptical roundabout interchange was one of the best interchanges studied in this research in relation to the frequency of conflicts as a total and the frequency of crossing conflicts.

In the literature, machine-learning techniques have been extensively implemented to capture the stochastic characteristics of freeway traffic speed. The deployment of intelligent transportation systems (ITSs) in recent decades offers much enriched and a wider range of traffic data, which makes it possible to adopt a variety of machine-learning methods to estimate traffic speed. However, an understanding of what type of machine-learning models to select for such applications and how to use probe vehicle data to estimate traffic conditions are still lacking. To fill this research gap, this study aims to utilize regression machine-learning algorithms to estimate traffic speed using probe vehicle and sensor detector data; also, the performance of the utilized machine-learning algorithms is compared using a novel traffic speed estimation framework. The results show that the proposed framework can effectively capture time-varying traffic patterns and has a superior ability to accurately estimate traffic speed in a timely manner. Using sensor detector data as the benchmark, the comparison results show that a random forest achieves the best performance in terms of traffic speed estimation.

The security and reliability of rail transit vehicles in service are greatly affected by their wheel–rail contact. Thus, modeling the wear of wheelsets and monitoring their status is important for improving safety and reducing costs. A key issue involves evaluating the wheelset status accurately and using this as the basis for developing effective maintenance strategies and measures. However, the open nature of actual wheel–rail systems and their inconsistent environmental conditions make it difficult to construct a precise theoretical model that covers both wheelset wear and status evaluation. In this paper, a synthesis approach for evaluating the wheelset health status of rail transit vehicles is proposed. The wheelset health status is defined, and then a data-driven wheelset health evaluation model is developed. Potential causes of deviations between the model and reality are analyzed based on a theoretical wear mechanism, and application prerequisites for the proposed model are given. A case study involving the Shanghai Metro shows that the proposed approach operates well and can, therefore, be applied in practice.