Journal of Transportation Engineering, Part A: Systems

On-ramp merging areas are congestion-prone segments of freeways. Depending on the aggressiveness of the driving population and the congestion level, the speed variance among travel lanes due to lane changes and ramp-merging flows may be so significant as to affect the optimal settings of deployed traffic control systems, such as metering rates or advisory speed limits. Extending from METANET, this study presents a lane group–based (LGB) traffic model to reflect the temporal and spatial distributions of traffic conditions among lane groups. The proposed model would allow traffic engineers to reliably assess the impacts of lane-changing activities in both upstream and downstream segments of an on-ramp area and better design their coordinated control strategies. To assess the effectiveness of the proposed model, this study has compared its performance with METANET under various traffic scenarios. The comparison results show that the proposed model can yield up to 26.9% improvement on the accuracy of predicting the temporal and spatial evolution of a freeway’s speed at the interchange area where freeway segments often experience extensive lane-changing activities due to on-ramp merging flows.

In the rail transit field, the practical operation process suffers from potential energy waste caused by disturbances. The present paper proposes a multiagent system (MAS) to reduce rail transit energy consumption when disturbances occur. The system is able to optimize speed trajectory and microscopic timetable for each train in near real time when disturbances occur. Two case studies have been carried out to investigate the feasibility and efficiency of the proposed methodology. In the first case study, three trains are simulated with 1,212 different scenarios with a disturbance that comes from the leading train. The results of those scenarios show that the proposed system is able to guarantee safety and has good potential in reducing energy consumption in such conditions. In the second case study, a train running among seven stations with potential delays is simulated. The result shows that each train agent can support a microscopic timetable optimization in near real time and results in a 13.40% energy savings. An additional 2,340 scenarios are simulated, and an average of 4.12% energy savings is achieved.

A method for estimating rail bending moments from relative vertical track deflection data measured by a train-mounted measurement system is presented in this paper. The novelty of the current study is that complete estimations of rail positive and negative bending moments from track deflection measurements are conducted by using a wavelet multiresolution analysis in conjunction with the radial basis function neural network considering the effects of varying track modulus. The simulation results show that the proposed framework can effectively employ vertical track deflections to estimate both maximum positive and negative bending moments in rails, with the average estimation error being 6.22% (i.e., 2.82 kNm). Moreover, the study confirms the capability of the train-mounted vertical track deflection measurement system (commercially known as MRail) in evaluating the rail bending moments over long distances.

The increase in e-bikes has aggravated traffic conflicts and casualties at intersections. However, existing signal control methods focus on motor vehicles, ignoring the influence of expansion behavior of e-bikes on motor vehicles. In order to better balance the traffic benefits between them, this paper proposes a new release mode named e-bike early green and establishes a signal timing model of mixed traffic flow considering e-bike early green (MTEG). In particular, an effect strength indictor that reflects the degree of influence of e-bikes on motor vehicles is put forward. Then, based on this indicator and e-bike ratio, the calculation model for e-bike early green time is built. The Nondominated Sorting Genetic Algorithm II is applied to solve the MTEG model that considers multi-objective optimization coordination. The results show that, when the e-bike ratio is in the range of 0.4–0.6, compared with the method proposed by the Transport and Road Research Laboratory (TRRL), the method proposed by the Australian Road Research Board (ARRB), and Improved-Webster method, the maximum improvement values of the MTEG method in cycle length, average vehicle delay, intersection capacity, and average stops per vehicle are $-5.56\%$, $-22.04\%$, $+2.30\%$, and $-8.00\%$, respectively. The outcomes provide a signal timing basis and technical support for a mixed traffic flow environment containing numerous e-bikes.

The objective of this paper is the creation of a suitable methodology capable of deploying elements that characterize the existing infrastructure on rural two-lane highways regarding the location and geometric features of intersections and evaluating the built-in road safety. The target is the utilization of these elements to lead to evaluation coefficients that show the hazard level of every intersection and every road section, highlighting the intersections of the road network with reduced safety level. In this context, a hierarchy of critical parameters influencing the risk of an intersection was made, along with appropriate mathematical equations based on the current regulations and corresponding crash reduction factors (CRFs) that have been presented in the literature. Initially, case studies were conducted, and special diagrams were produced to assess the hazard level of each parameter. Then, basic statistical analysis of the results was made using regression factors to develop an appropriate mathematical model for the estimation of the hazard level. A corresponding procedure was performed for each parameter separately, and appropriate coefficients were sought. These coefficients were summed up, and a total score characterizing each intersection has been calculated, leading to a ranking in respect with the hazard level. Finally, data were collected on a significant sample of the existing road network of Greece, about 1,000 km of road network with more than 4,000 intersections (unpaved road intersections are also included), in order to evaluate the results of the proposed methodology. Part of the results has been interpreted with real accident data collected from the police department, showing that there is a correlation between them. This correlation must be confirmed in further research by a statistical analysis between the results of the present methodology and the actual recorded accidents.

Connected and automated vehicle (CAV) exclusive lanes are an important application of intelligent transportation technologies on highways. However, current research on CAV exclusive lanes mainly focuses on basic road segments rather than on on-ramp junctions. This paper analyzed the impact of CAV exclusive lanes on the traffic capacity of on-ramp junctions through theoretical analysis and numerical simulation. This paper established a capacity model for heterogeneous traffic consisting of CAVs and manual vehicles (MVs) considering the CAV degradation mechanism. Three key parameters, namely the CAV penetration rate, the proportion of CAVs on regular lanes, and a CAV platoon coefficient, were introduced into the heterogeneous traffic capacity model. Based on this input, the relationship between traffic capacity and the three parameters was analyzed on regular lanes. The critical CAV penetration rate at which a CAV exclusive lane reaches saturation was expressed mathematically. An on-ramp junction scenario with a CAV exclusive lane was simulated numerically with microscopic heterogeneous traffic flow models. Experimental results showed that congestion at the junction area no longer spread upstream after the CAV penetration rate exceeded 70% without CAV exclusive lanes. However, for the case in which a CAV exclusive lane was used, CAVs on the exclusive lane traveled quickly through the junction and effectively relieved traffic congestion when the CAV penetration rate reached 40%. The results also showed that a long acceleration lane slightly reduced the CAV platoon coefficient. In addition, a CAV exclusive lane improved traffic capacity when the CAV penetration rate was higher than 30%, regardless of the length of the acceleration lane.

Traffic flow estimation (TFE) plays an important role in transportation systems, especially in relation to the detection, planning, and management of traffic for expressway systems. However, traditional approaches to TFE using roadside detectors usually have low-coverage and high-cost problems. This paper proposes a toll ticket data (TTD)–based approach to TFE for filling the gaps in existing approaches and also considers multitype vehicles’ impact on traffic flow for more accurate TFE results. First, route choice models are constructed to capture travel characteristics of mult-type vehicles and acquire route traffic flow from TTD. Then, a simulation-based dynamic traffic assignment (DTA) algorithm is developed to obtain traffic flow regarding vehicle type, time, and location, with the consideration of multitype vehicles’ impact by using a tollbooth conversion factor (TCF) and point-of-queue model (PQM). The proposed approach is evaluated empirically using data relating to the Shandong Expressway Toll System in China. The results confirm that it performs effectively. This research could contribute to future TFE and provide references for dynamic, delicate traffic management in an economical and reliable way.

Recently, there has been a significant amount of research related to heavy trucks operating as connected and autonomous vehicles (CAVs). As argued in this paper, to understand the potential impact on the freeway system of CAV technologies, analyses should be conducted using the standard US methodological framework. Consequently, this paper uses the exact Highway Capacity Manual, Sixth Edition (HCM-6) equal capacity passenger car equivalent (EC-PCE) methodology to estimate capacity and EC-PCEs for CAV truck platoons on freeway segments. It was found that EC-PCE values for CAV trucks are, on average, 34.3% lower compared to the values for non-CAV trucks, indicating that CAV platoons can have a positive effect on highway capacity. The amount of decrease is a function of a number of CAV operational assumptions, and these are studied through a sensitivity analysis. This paper demonstrates that the effect of CAV truck platoons can be modeled using the standard HCM-6 approach, and the methodology allows meaningful comparisons so that traffic agencies can better prepare for the adoption of CAV technologies.

Snowplow trucks serve a crucial role in winter maintenance activities by removing, loading, and disposing of snow. A performance-monitoring and analysis process can assist transportation agencies in effectively managing snowplow trucks and maintaining the normal functioning of roadways. Previous literature suggests that most snowplow truck performance analysis is done through life-cycle cost assessment at the macro level to determine the optimal life cycle for the entire truck fleet. However, this can lead to a waste of resources and may incur bias due to ignorance of performance variations resulting from endogenous and exogenous features. More important, such analysis fails to identify the factors that contribute to performance deterioration. With the proliferation of operational data on snowplow operations, these concerns can be addressed through predictive machine-learning (ML) techniques in a data-driven fashion. In this study, we applied a popular ML technique, random forest (RF), to predict the performance of snowplow trucks, which was quantified via the rank of major repair times. Another ML technique, linear support vector machine (SVM), was also applied for benchmarking and comparison. Using the snowplow truck fleet managed by the Utah Department of Transportation (UDOT), both models were implemented and it was demonstrated that RF outperforms linear SVM with regard to prediction accuracy. Further, a feature importance analysis from the RF model can help transportation agencies to improve truck replacement strategies by identifying crucial performance factors. Lastly, a sample application of the developed prediction model using RF suggests the threshold of work intensity for preventing the rapid deterioration of truck performance in various working environments. Compared with the life-cycle cost analyses used in previous studies, the prediction model proposed here can help transportation agencies to better prioritize fleet replacement.

The use of nonmotorized modes of transportation, mainly cycling and walking, is essential for a sustainable transportation system. University campuses, considered important trip generation hubs (TGHs), because they promote a large number of trips, need to have adequate infrastructure to encourage the use of nonmotorized modes. The present study proposed a method to calculate an index from the data on nonmotorized modes, used in university campuses, through the mapping of field attributes, allied to a geographic information system for the application of multicriteria decision analysis (MCDA), emphasizing the comfort and safety of users. The following criteria, among factors and restriction, were proposed: the length of cycling structures, the physical characteristics of cycling infrastructure, the connectivity of cycling infrastructure, the location of bicycle parking lots, parking spaces for bicycles, road signs for nonmotorized modes, roads with sidewalks, and physical-territorial fragmentation. Among the criteria analyzed, the one of roads with sidewalks in the campus achieved the best evaluation, because of the higher level of adequacy for the calculation of the index. The first three infrastructure-related criteria for cycle tracks, cycle lanes, cycle routes, and shared use paths were not scored because of the short length of bike ways and the lack of other cycling infrastructure in the studied area. The representation of the results in thematic maps made it possible to identify, in the various sectors of the surveyed area, which places need more interference to meet the minimum safety and comfort conditions of users of nonmotorized modes.

The study attempts to investigate crossing conflicts at unsignalized T-intersections under mixed traffic conditions using post encroachment time (PET) as a surrogate safety measure (SSM). To comprehend the objective, data for four unsignalized T-intersections with varying intersection geometry (with and without Central Island) was collected using videography techniques. Primarily, PET datasets were extracted in the lab from the recorded video and were checked for their probability distributions. Among all the potential distributions, generalized extreme value (GEV) distribution was observed to be the best-fitted distribution. Further investigations revealed that the percentage of critical crossing conflicts (PCCC) was higher with the presence of motorized two-wheelers (2W) and motorized three-wheelers (3W) in the traffic stream. This was followed by cars, buses, LCVs, and trucks. Among vehicle type combinations (conflicting-offending), 2W-2W, 2W-3W, 3W-2W, and 3W-3W were identified as the most critical cases for the subject study locations. Further, it was observed that the volume of conflicting stream and proportion of 2Ws in the conflicting stream has a significant effect on PCCC. The obtained PCCC, when compared among the subject study locations for similar traffic flow characteristics, revealed a significant difference between them, highlighting the effect of intersection geometry on traffic safety. The severity of conflicts was delineated into four levels by correlating PET values with the speed of conflicting vehicles. The developed severity levels, compared with the field-recorded crash data are found to be in close approximation. This establishes PET as a valid SSM.