Journal of Transportation Engineering, Part A: Systems

Truck platooning is a truck operation system in which one or more trucks closely follow a lead truck. The system is advantageous for road freight and promises to contribute to developing the next generation of road transportation due to its many benefits, including reduced fuel consumption and emissions and improved traffic flow. However, truck platooning also has some potential disadvantages, such as the occlusion of post-mounted road signs. In this study, a VisSim simulation model was used to evaluate truck platooning’s impact on road sign occlusion on four-lane and six-lane freeways. A regression model was then used to investigate road signs’ occlusion by conventional trucks and platooned trucks. The regression model found that sign occlusion increases with increasing the number of lanes on the highway, headway within a truck platoon, traffic demand ($\mathrm{veh}/\mathrm{h}/\ln$), truck percentage, and platooning penetration (%). Conversely, a higher number of trucks in a platoon (platoon length) decreases sign occlusion because it reduces the overall number of truck platoons, including individual trucks. A 100% truck platoon penetration particularly caused sign occlusion for up to 82.3% of other vehicles under certain conditions. This study’s findings demonstrate the need for transportation agencies to include countermeasures for mitigating sign occlusion before deploying truck platooning.

To improve public transportation operations, this research investigates the lane-changing behaviors for buses exiting at bus bay stops. Game theory is utilized to analyze the interactions between bus and social vehicle drivers. A two-player noncooperative, non-zero-sum mixed-strategy game model is formulated to consider the time saving and collision avoidance payoffs for both players in the game. The characteristic of mass passengers carried by buses is also taken into account in the payoffs. The maximum likelihood estimation is used to calibrate the formulated model using the vehicle trajectory data collected from Lvboqiao bus bay stop, Dalian, China. The validation results reveal that the formulated model can effectively predict the lane-changing decisions with a high level of accuracy. Besides mixed strategies, the players in the game may have dominant strategies, which are the best choices that have nothing to do with the strategies that other players may adopt. In both mixed and dominant strategies, bus drivers are more inclined to choose the strategy of not changing a lane, and social vehicle drivers tend to choose the strategy of not giving way to the bus. The tendencies to choose strategies by both players are consistent with the actual decisions made by drivers. It is proven that the formulated game model is highly reliable to describe bus drivers’ exiting behaviors at bus bay stops and can offer the decent theoretical support for both players to make better decisions.

The outbreak of coronavirus disease 2019 (COVID-19) disrupted our regular life. Many local authorities enforced a cordon sanitaire to contain the rapid spread of the disease. Travelers can only pass the cordon after the disease screening. This paper aims to propose a method to design a traffic inflow control scheme for onramp metering to maximize urban freeway network throughput with an endurable queuing delay constraint at all offramps around cordon sanitaire. A bilevel programming model is formulated where the lower level is transportation system equilibrium to predict link traffic flows. The upper level is onramp metering optimization that is nonlinear programming. A stochastic queuing model is used to represent the waiting phenomenon at each offramp where the disease testing is conducted. A heuristic algorithm is designed to solve the proposed bilevel programming model where the method of successive averages (MSA) is adopted for the lower-level model, and a genetic algorithm (GA) with elite strategy is adopted for the upper-level model. An experimental study is conducted to demonstrate the effectiveness of the proposed method and algorithm. The results show that these methods can find a good heuristic optimal solution. These methods are useful for freeway operators to determine the optimal onramp control for disease control and prevention. They are also beneficial to set up checkpoints for all kinds of risks such as drunk driving and criminal searching.

With the rapidly changing landscape for taxis, ride-hailing, and ride-sourcing services, public agencies have an urgent need to understand how such new services impact social welfare: impacts of technologies on matching customers to service providers, evaluating ride-sourcing operations, and evaluating surge pricing policy, among others. We conduct an empirical study to answer this question for Uber using a dynamic spatial equilibrium taxi-matching model. Given a matching function, the spatial distribution of demand activities, and service coverage, the model outputs equilibrium fleet sizes, matches, and social welfare by zone and time of day. Uber provides pickup data for a specific time period in New York City (NYC). Parameters from the model calibrated from medallion cab (Taxi) data are grafted onto the Uber model to supplement the missing information. The Uber model has a root-mean square error of 7.75 matches/zone/interval, which is approximately an 8.52% error. Spatial distribution of responses in demand to fare hikes or vehicle supply to demand surges measurably differ between NYC Taxi and Uber markets. Baseline estimations of welfare indicate that the NYC Taxi industry generates $495,900 in consumer surplus and $1,022,400 in Taxi profits for the 4-h interval, while for the Uber market, the model estimates $73,300 in consumer surplus and $151,300 in Uber profits during the same interval. Spatial-temporal dynamics resulting from fare hike and congestion fee scenarios are analyzed to determine requirements for allocating the congestion charge revenues toward public transit to maintain or improve upon the same consumer surplus.

To minimize the likelihood of having rear-end collisions at signalized intersections, this study presents two algorithms for use with an actuated signal controller and the wide-ranger sensor, deployed for dynamic all-red extension. The first algorithm features its replacement of conventional gap-out control with vehicles detected in a field-calibrated dilemma zone and executes a phase transition only if no vehicle is within the detected zone. To further assure no rear-end collisions occur during either the defaulted green max-out or extended max-out, this study has further proposed a dynamic two-stage control algorithm that can optimally divide the duration between the minimum and maximum greens into two stages, where the signal within stage-1 duration will operate the first algorithm for green termination to ensure its efficiency, but shall execute the phase transition within stage-2 only at the time point projected to have the least likelihood of incurring rear-end collisions between any pairs of leading-following vehicles within the sensor’s monitoring zone. Because the effectiveness of such algorithms depends on the accuracy and reliability of their key parameters, the study has detailed their operational logic and the procedures for system calibration with field data, traffic simulator, and optimal searching heuristics. A predeployment evaluation at one candidate intersection has also been conducted with field data and a well-calibrated traffic simulator. Results of extensive experimental analyses with both peak and midday data convincingly confirm the effectiveness of both proposed algorithms, based on the surrogate variable of vehicle conflicts for potential read-end collisions as used both in research and practice.

Increasing demand for high speed railway lines necessitates reducing construction and maintenance costs. Accordingly, RC slab tracks are preferred due to their advantages such as being almost maintenance free and supplying uniform support conditions. However, new testing procedures are required for the performance evaluation of the slab tracks to identify possible failure modes which may not be observed in numerical analyses. Although there are some procedures in the literature, there still is no consensus on loading intensity, shape, frequency, or number of cycles. A testing procedure that greatly decreases experimental costs was adopted for the design approval tests of precast RC slab tracks. The procedure was evaluated experimentally through full-scale laboratory tests of intact and intentionally damaged specimens. The study demonstrated that the proposed testing procedure reduces experimental costs and identifies the mechanical properties of slab tracks.

Efforts were initiated through the Strategic Highway Research Program (SHRP) to quantify level of service (LOS) based on travel time and travel time reliability. To contribute to this goal, this study adopted a microscopic simulation-based method and researched the applicability of average travel time (ATT), planning time index (PTI), and buffer time index (BTI) to quantify freeway weaving section, merging area, and diverging area operational performance by the posted speed limit and number of lanes. A calibrated microscopic traffic simulation model was developed and validated using existing conditions data and then modified to generate density and travel time as outputs for various hypothetical analytical scenarios. Each simulation scenario included weaving sections, merging areas, and diverging areas with different posted speed limits, number of lanes, and traffic volumes. The ATT per 1.61 km (1 mi), PTI, and BTI were computed and compared with the density to examine the relationships and identify thresholds by the posted speed limit and number of lanes. The ATT per 1.61 km (1 mi) thresholds increase from LOS A through LOS F, whereas the PTI and BTI thresholds increase from LOS A through LOS D or LOS E and decrease thereafter. The ATT per 1.61 km (1 mi) thresholds increase as the posted speed limit decreases or the number of lanes decreases. However, the PTI and BTI thresholds could decrease as the posted speed limit decreases. The trends are fairly consistent for weaving sections, merging areas, and diverging areas. The differences in thresholds for merging and diverging areas, merging areas, and diverging areas indicate that separate thresholds are needed for merging areas and diverging areas, unlike what is currently provided in the Highway Capacity Manual (HCM). Caution must be exercised when applying PTI and BTI thresholds for very uncongested or congested conditions. It is, therefore, recommended to categorize the PTI and BTI thresholds as highly reliable, moderately reliable, and unreliable, unlike the standard six ATT per 1.61 km (1 mi) or density LOS A through F categories. Alternatively, they could be used in conjunction with ATT per 1.61 km (1 mi) or density thresholds to assess congestion or reliability-related affects.

This paper presents an on-board advance warning system based on a probabilistic prediction model that advises vehicles approaching a freeway diverge area on when to change lanes to reach an off-ramp, with the goal of reducing delay caused by congestion at the diverge area. The prediction model estimates the probability of reaching a goal state on the road using one or multiple lane changes. This estimate is based on several traffic- and driver-related parameters such as the distribution of intervehicle headway distances and lane-changing maneuver duration. For an upcoming off-ramp, the advance warning system utilizes the prediction model to continuously calculate the probability of reaching that off-ramp and advises vehicles to change lanes when the probability drops below a certain threshold. To evaluate the impact of the proposed system on reducing traffic delay at congested freeway diverge areas, it was used in a simulation case study of a two-lane segment of the I-66 interstate highway to advise vehicles taking an off-ramp on when to change lanes. The results indicate that incorporating the proposed system can reduce the average delay by up to 83%—effectively wiping out the congestion in some cases—depending on traffic flow and the ratio of vehicles taking the off-ramp.

A method is proposed for optimizing the restoration sequence of damaged components in a disrupted rail freight network. Given the network’s demand matrix and capacity settings, a mixed-integer linear program (MILP) model is developed for assigning capacitated freight flows and minimizing total hourly costs. The cumulative cost increment (excess) is computed for each examined restoration sequence in a disruption scenario, using the duration of each restoration step and minimized hourly costs in intermediate network states from the MILP. A simple genetic algorithm (GA) is applied for finding the restoration sequence that minimizes the excess after a disruption event. A numerical case with a small network and a disruption scenario is synthesized to demonstrate this method. The optimized restoration sequence and schedule found by the GA are proved globally optimal in this case by exhaustive enumeration. The GA’s effectiveness is further verified with three additional disruption scenarios. A sensitivity analysis shows that the minimized excess is more sensitive to capacities of damaged components when capacity levels of components or upper limits of travel time are lower. The minimized excess is highly sensitive to the demand level when freight flows are moderately undersaturated. It is also found that restorations of damaged nodes should have higher priorities at lower capacities of damaged nodes, higher capacities of damaged links, and higher unit costs of alternate shipments by trucks.

For better refined management of urban expressway traffic, it is necessary to quantitatively analyze the impact of ramps on lane-operational capacity and lane flow distribution at the bottleneck of an urban expressway. Employing the survival analysis method and the maximum value method, this paper calculates the lane-operational capacity before and after the traffic flow breakdown. And then lane-operational capacity, lane flow distribution of the expressway section, and the relationship with the ramp position are analyzed. Through the analysis of actual data, several interesting conclusions are drawn: (1) the lane-operational capacity of the inner lane is higher than that of the outer lane on the two-lane section without ramp. The lane-operational capacity of the three-lane section without ramp is ranked as middle lane, inner lane, and outer lane; (2) on-ramp has little influence on the lane-operational capacity of the expressway section. The inner lane of the expressway section around the on-ramp has a larger operational capacity than outer lane, consistent with the section without a ramp. The lane-operational capacity of the expressway section around the on-ramp decreases slightly both on the inner lane and outer lane; (3) the off-ramp has considerable influence on the lane-operational capacity of the expressway section. There is a significant reduction in lane-operational capacity of the expressway section outer lane around the off-ramp. On the expressway section after the off-ramp, the lane-operational capacity of the outer lane is higher than that of the inner lane, which is opposite to the section without a ramp; and (4) ramps have significant impact on lane flow distribution. On the sections without ramp, the lane with the largest capacity bears more traffic flow, and lane flow distribution is shaped like a bracket. On the sections around the ramps, lane flow distribution is shaped like a scissor.

This paper presents an in-depth case study of a heavy-haul railway line in Sweden to analyze the twist and longitudinal level geometry defects. A linear model was applied to model the evolution of the amplitude of the longitudinal level defects and twist over time. Despite the effect of the defect shapes on the dynamic track loads, the amplitude of the defects still is the only criterion used for the assessment of geometry defect severity. The application of first- and second-order derivatives to capture information about the shape of defects was investigated in the case study. In addition, the RUSBoost algorithm was used to classify track sections into healthy and unhealthy sections using the imbalance class data set. In this algorithm, the standard deviation and the kurtosis of the geometry parameters were used as explanatory variables. Finally, the abnormal track geometry degradation patterns identified in the case study were explored in detail. The results of the analysis can be used directly in maintenance modeling and used for the purpose of maintenance scheduling.

Understanding users’ attitudes toward national-level road pricing schemes is crucial for successful implementation. Despite the European Union (EU) policy agenda, there is a gap in understanding users’ attitudes toward distance-based (DB) and time-based (TB) pricing considering features of pricing technology, willingness to pay, and previous pricing scheme experiences. This gap is especially prominent in the southern and eastern European countries. This research explores factors affecting the maximum acceptable price for DB and TB pricing, as well as users’ experience and attitudes, using a case study of North Macedonia. The analysis framework uses statistical modeling of questionnaire data, including structural equation modeling. The findings show that the TB concept is more suitable for daily users, while a DB scheme is more suitable for less frequent motorway users. In addition to use frequency, income and previous experience with pricing technology were the strongest predictors of willingness to pay. The findings provide lessons on the distributive benefits and burdens of DB and TB schemes, as well as their implications for policy learning by considering context-dependent trade-offs between user and policymaker perspectives in medium-income transition European countries.