Journal of Transportation Engineering, Part A: Systems

Mud pumping is a major problem for high-speed rail (HSR) slab tracks because it can cause deterioration of track alignment and further compromise operation safety and ride comfort of passengers. Timely inspection for mud pumping is crucial to the operational safety of high-speed rail networks. This paper proposes a novel detection method for mud pumping defects based on the measurement data of track geometry. The time and frequency features of the surface irregularity signal corresponding to the mud pumping sites were first analyzed. The concave anomalies reflecting the features of mud pumping and the peaks at the wavelength of 5 m and its octave on the power spectrum density (PSD) curve were revealed. On this basis, to automatically detect mud pumping defects, a multiscale signal decomposition method was employed and defect-sensitive features were extracted. A mud pumping index (MPI) indicating the severity of mud pumping was then established, and a three-level management scheme for mud pumping maintenance was developed accordingly. The performance of the proposed detection method was verified against visual inspection recorded on an in-service HSR line, which was constructed with the China Railway Track System (CRTS) I slab track. The results show that the proposed detection method can effectively locate and assess the mud pumping defects with acceptable accuracy for HSR track maintenance needs.

Automated vehicle (AV) technology is expected to make roads safer. However, until recently only a handful of studies could test such hypotheses due to limited access to testing data. This study contributes to the literature by jointly analyzing the associated factors of three interrelated outcome variables—vehicle at fault, collision type, and injury outcome in AV-involved crashes. We use Bayesian networks to analyze the manually extracted data from reports of 333 AV-involved crashes that occurred in California between January 2017 and October 2021. The summary statistics indicate that rear-end collisions are the dominant (63.5%), while AVs are at fault for a small proportion of crashes (14.4%), and a majority of crashes (84.4%) are noninjury. The joint inferences of the Bayesian networks show that irrespective of the collision type, when the AV is at fault, the chance of the physical injury in a crash increases significantly. Further, the chance of an AV being at fault seems higher in parking locations, and during driving at wet pavements in unclear weather. The chances of AV rear-end collisions are lower in the parking lot, and when nonvehicular participants are involved but increase in high traffic flow roadways. We also find that the likelihood of physical injury is higher at high-speed locations, intersections, and wet pavements. These insights suggest specific areas (unsignalized intersections, less structured right-of-way rules, and wet pavements) where technological improvements could enhance the safety performance of AVs.

The ethical implications for the engineering profession of the development and deployment of automated vehicles (AVs) can be explored by analyzing the implications of AVs across three major socio-technical systems—technology, transportation systems, and policy. Mapping the ethical canons of professional engineering societies to these domains provides a lens to investigating existing ethical issues and uncovering issues that still need attention. The codes of ethics for five engineering societies direct engineers to consider, identify, mitigate, and manage how their work affects the public. AV ethics literature in the technology domain has focused mainly on crashes, AV software capabilities, and hardware. This narrow focus signifies that engineers in the technology domain can do more to understand potential impacts beyond AV crash behavior. In the transportation systems domain, among the many ethical issues affected by AVs, how engineers design and deploy surface transportation infrastructure is an example of an ethical system-level problem yet to be addressed. Lastly, the policy domain has begun addressing primary effects like protecting the public from physical harm, but other ethical aspects remain unaddressed. All three domains could benefit from more holistic system-level assessments of the ethical implications of AVs. Engineers can use their professional engineering organization ethical canons to evaluate their contribution to managing ethical issues in these AV domains and improve how automated vehicles serve and safeguard the public.

Real-time and accurate short-term traffic flow prediction is important for the operation and management of expressways. This paper presents a hybrid model that can be used to discover the spatiotemporal dependencies of traffic flows and, thus, achieve a more accurate traffic flow forecast. This model stacks a full connection (FC) layer, two-layer long short-term memory (LSTM), and a middle mean pooling layer, denoted by FC-LSTM, to expand the ability of LSTM to capture spatial correlations and too long-term temporal dependencies of traffic flows. Traffic data of 51 toll stations (accounting for 15% of all stations) in the Guizhou expressway network in China in January 2016 were used to evaluate the validation of FC-LSTM. The results showed that the traffic flows at adjacent tollgates were interactional and correlated, that FC-LSTM could capture this spatial dependency and the temporal correlations of traffic flows, and, thus, that it was superior to other baselines with a low prediction error, high precision, and high fitting degree. Moreover, FC-LSTM is interpretable and robust owing to its explicit input and is suitable for traffic flow prediction for most tollgates under the same parameters.

In this research study, a multistage fuzzy logic controller (MS-FLC) was developed for traffic control in incident management on expressways. The MS-FLC serves as the traffic operator’s decision-making support tool at the operational level. The MS-FLC gathers real-time traffic and incident data to analyze and predict traffic conditions, as well as to recommend alternative control measures to the traffic operator in the form of linguistic expressions. The MS-FLC is embedded in a traffic simulator controller (TSC) prototype, and was evaluated by comparing its performance with no control scenario and ALINEA\Q, a popular local ramp control algorithm, across several incident scenarios in a simulation environment. In general, MS-FLC outperformed ALINEA\Q with respect to global objectives. In particular, whereas the ALINEA\Q algorithm favors the mainline, the MS-FLC algorithm significantly improves mainline travel conditions while substantially reducing ramp queues. It is concluded that, if properly designed, the MS-FLC can be a robust tool for traffic control on expressways under incident conditions.

The sustainability of transportation networks is threatened by growing amounts of emissions generated by vehicles. Urban freight delivery is one of the major sources of these emissions. Cargo cycles are attracting increasing attention for urban delivery owing to their numerous benefits, such as being less pollutant, requiring less space on roads, and high maneuverability. Electric vans have also been proposed as another green mode of freight delivery with a larger delivery capacity and range relative to cargo cycles. In this study, urban freight delivery is targeted through a heterogeneous fleet vehicle routing problem. The proposed framework is used to capture the optimal configuration of a mixed freight delivery fleet offering cargo cycles (i.e., pedal-assisted bikes and trikes as well as electric bikes and trikes) alongside the often-used diesel or gasoline vans or small trucks and electric vans. The formulated problem aims to minimize the delivery costs for companies, including maintenance, purchase, fuel, and labor costs and noise and emission taxes. The methodological contribution of this study is to develop a new metaheuristic algorithm, based on variable neighborhood search and simulated annealing, that finds a better solution with up to 7% lower objective function value and 50% to 80% lower solution times relative to the existing approaches in the literature. This study also demonstrates that bikes and trikes are selected at short delivery distances in the Chicago downtown network. Electric vans are more suitable for large distances, while cargo vans are selected for medium distances. Imposing an emission tax would change the model from cargo vans to electric vans. The results also show that increasing tax levels does not necessarily reduce societal costs because the delivery company tends to maintain its profit margin by increasing delivery costs for customers.

The transit signal priority (TSP) strategy has been widely adopted as a practical approach to improving the efficiency and reliability of transit operations. Over the years, few studies have adopted the concept of TSP to implement freight signal priority (FSP) for improving the safety and operational performances of freight vehicles. Despite the promising outcome in previous studies, several drawbacks, such as inaccurate estimation of a freight’s arrival time at a stop bar and inefficient use of priority measures, have prevented their wide applications. This paper aims to develop a FSP system that utilizes emerging connected vehicle technology to overcome the challenges associated with conventional FSP systems. An estimated time of arrival (ETA)-based FSP logic was developed and analytically examined to demonstrate the operational efficiency that can be achieved. The proposed FSP system was implemented in a real-world coordinated signalized corridor for systematical analysis and validation of its field operation. Analysis results showed that the proposed FSP system can effectively address the shortcomings in traditional FSP systems by accurately estimating a freight’s arrival time and providing accurate and efficient priority measures.

Traffic congestion has become a significant problem that hinders the proper functioning of the transportation system. Traffic congestion forewarning methods can provide traffic management with accurate congestion prediction information, thus taking timely measures to avoid or alleviate traffic congestion. In this paper, we propose a data-plus-model framework-based expressway congestion forewarning method that can be used in the absence of high-quantity and high-quality data. First, we applied historical traffic flow data to determine the approximate extent of the traffic congestion forewarning and fitted a proper traffic fundamental diagram to characterize the traffic flow. Then, the critical congestion forewarning parameters were obtained according to the slope change of the fundamental diagram. Finally, the proposed method was applied to an expressway section in Beijing to forewarn of traffic congestion with multiday historical traffic data. The experimental results show that the proposed method can effectively analyze the trend of traffic flow accurately and quickly give early forewarning of congestion, which is helpful in reducing the occurrence and persistence of congestion.

The turnout is a vital railway infrastructure. Setting reasonable reduced values of the switch rail is the key to ensuring high stability and security when a vehicle passes a turnout. Poor reduced value condition leads to a car body swing or even derailment. To improve the turnout performance and reduce maintenance costs, of great importance is studying the reduced value. However, the current research did not consider wear rail profiles, and most optimizations were based on the given reduced values. Therefore, to promote dynamic performances as a vehicle passes through the turnout and to improve the traditional optimization method for the reduced value, the following investigations were carried out in this study. First, the tracking measurement of the rail profiles was conducted, and the variation law of the rail wear and the reduced value were analyzed. Further, based on the vehicle-turnout coupled model, an optimization model for the reduced value of the switch rail by the multi-island genetic algorithm was proposed. This model aims to minimize the dynamic responses. The objective functions of a vehicle passing a turnout in the main and diverging route were determined under the constraint conditions of the monotonicity of the reduced values. Finally, the optimizations were carried out in cases in which a vehicle passes on the main and diverging routes. The optimal results indicate that, after optimization, the objective function for the main and diverging routes decreased by 33.66% and 11.92%, respectively, illustrating the effectiveness of the optimization model.

The track is the basis of railway and subway train operations. Under the repeated action of traffic load and the influence of line structure, geographical environment, and other factors, the condition of the track facility deteriorates, affecting driving safety, shortening the service life of the facility, and increasing maintenance costs. Therefore, it is of great significance to investigate the optimization of rail facility network life cycle repair decisions and devise a reasonable repair plan for scientifically controlling the repair cost under the prerequisite of ensuring operational safety. This study focused on optimizing track facility network life cycle repair decisions comprehensively considering the heterogeneity, uncertainty, and linkage of rail facility degradation. The minimum life cycle cost of the facility network was considered as the optimization objective, and an adaptive learning (AL) mechanism-based maximum likelihood estimation (MLE) method was developed. A network-level life cycle repair decision optimization model based on an AL-Markov decision process (AL-MDP) was constructed and solved by using the Lpsolve toolkit in MATLAB. The Beijing Metro facility network, which is composed of small-radius curved rail units, was considered as an example, and based on a simulation of the network state using the Monte Carlo method, the proposed network-level AL-MDP model and network-level MDP model without AL were used to optimize repair decisions with a planning cycle of 10 years. The results were compared and analyzed, demonstrating that the proposed network-level AL-MDP model can effectively improve the quality of facility network repair decisions compared with the MDP model and that it has higher practicability.

The emergence of online taxi-hailing fills the shortages of the taxi supply, but the reservable feature of online taxi-hailing has led to the increment of road parking, which has aggravated traffic congestion. Improving the prediction accuracy of online taxi-hailing demand is crucial to reducing congestion. Moreover, the traffic demand of different modes that influence each other is affected simultaneously by the environment, land-use characteristics, and geographic location. Therefore, we introduced a forecasting framework to improve online taxi-hailing demand forecasting accuracy based on multimode traffic in metro station areas and explored the best predictive range of metro station areas with different land-use characteristics. The paper extracted the origin and destination (OD) information from taxi, online taxi-hailing, and metro data. Next, we extracted the essential factors from the environmental data through Pearson’s coefficient analysis. Finally, we selected the best predictive model from several models that contain different information and explored the best predictive range. The result indicates that multisource traffic data and considering multimode traffic could improve traffic demand prediction accuracy. Furthermore, we found that the best traffic demand predictive ranges in metro station areas with different land-use characteristics are different.

The present study focuses on examining the potential of accepted gaps to assess traffic safety at unsignalized T-intersections by estimating the likelihood of a crash. Four unsignalized T-intersections with varying geometry from four different cities in India were selected. Postencroachment time (PET), a surrogate safety measure, was included in the present study to validate the results obtained through the accepted gap. The critical gap was devised as a potential parameter to evaluate traffic safety based on accepted gaps. Various probability distributions were checked for defining accepted gap and PET datasets. Generalized extreme value (GEV) was found to be the best-fitted distribution defining both: accepted gap and PET datasets. Thereafter, GEV distribution was used to estimate the likelihood of crash using accepted gap and PET datasets. The statistical check showed no significant difference in the crash probability obtained using the accepted gap and PET. Thus, the study was further extended, and risk characterization was performed using accepted gap data. The risk is characterized into different levels using the $K\text{-}\text{means}$ clustering technique. The results attained were validated with 3 years’ crash data obtained from Surat police authorities. The developed risk thresholds provided a close approximation with the crash data. Thus, it can be concluded that accepted gaps can be considered as a potential surrogate safety measure for analyzing the risk and severity of crossing conflicts at unsignalized intersections.