Pressure-based traffic signal control and actuation-based traffic signal control have some desirable properties such as decentralized control, the capability to make continuous decisions, the ability to address changes in demand, and overall intuitiveness. However, the phase gap-out method in actuated control often leads to unused green time, while pressure-based control does not conform to the NEMA ring-barrier structure. Considering the benefits of both methods, this study proposes an integrated control approach where the phase gap-out logic in actuated control is replaced with a pressure-based extension method. This control strategy uses vehicle trajectory data and incorporates two definitions of pressure: one based on the number of vehicles and the other using estimated time of arrival–weighted pressure. The control methods are further enhanced by several objective-driven strategies: dilemma zone (DZ) protection to reduce rear-end crashes, queue clearance (QC) to improve user expectancy, and secondary extension (SE) to improve progression along the arterial. The proposed methods are compared against two conventional control strategies (fully actuated control and coordinated-actuated control) and three trajectory-based actuated control methods. Simulations were conducted on an arterial network under both moderate and near-saturated volume scenarios. Results suggest that under moderate conditions, the pressure-based control methods outperformed conventional controls, while the integration of QC was necessary to improve performance in near-saturated conditions, whereas DZ protection and the SE method achieved the objectives of safer phase termination and smoother flow. These results demonstrate the potential of integrating pressure-based logic with actuated control for safer and more efficient signal operations.
The spatially constrained environment of tunnels presents significant challenges for guide sign installation, raising questions about the necessity of including exit numbering information alongside place names. This study investigates the impact of simplified tunnel exit signs on drivers’ visual recognition performance through a controlled experimental design. Three sign conditions were compared: simplified signs inside tunnels (place name only), standard exit signs inside tunnels (place name and exit numbering), and conventional signs outside tunnels (open-road baseline condition). Twenty-six licensed drivers participated in three progressive experiments focusing on information retrieval, integration, and consistency judgment. Results demonstrated that simplified signs (place name only) achieved the highest information retrieval efficiency, with significantly reduced reaction times. When place name information was sufficient, exit numbering acted as redundant cues, increasing cognitive load; incongruent number information led to significantly lower accuracy. Notably, many drivers completely ignored exit numbering information, showing significantly greater reliance on place names in tunnel environments. These findings suggest that place name-only simplified signs may improve information processing efficiency in tunnels, and motivate further validation in higher-fidelity driving contexts to inform sign design in spatially limited settings.
Traffic laws work successfully when they are both enforceable and accepted by the public. In many low- and middle-income countries, however, rapid urban change, limited enforcement capacity, and short-term personal incentives make certain rules difficult to uphold. Moreover, short-term personal incentives often dominate the strategic interactions between citizens and law enforcers, leading citizens to violate laws and law enforcers to overlook violations. This study introduces a practical, data-driven framework for illustrating how such laws can be diagnosed and how strategic interactions can contribute to enforcement failure. Using 496 citizen-generated posts from the Facebook group Road Planners Bangladesh, the study applied transformer-based sentiment classification and a domain-specific emotion lexicon to filter and rank traffic law violations. Sixteen critical cases were identified, and Case V176 (autorickshaw driving on a national highway) showed the highest public concern with a Composite Intensity Index of 94/100. The selected case was modeled as a two-player strategic interaction between a violator and an enforcer using ambassador-derived and analytic hierarchy process-weighted payoff matrices. In both models, Nash equilibrium analysis produced a stable Violate–Ignore outcome, showing that the rule persists in a misaligned incentive environment rather than from random disobedience. Backward induction was then used to identify the specific incentive leverage point at which behavior can shift toward compliance—analogous to a codesign intervention within a sociotechnical system. The results show that raising the perceived risk of being caught, rather than increasing fines, was the most effective modeled lever for shifting the equilibrium in the specific case. To support implementation, the study proposes a two-step recommendation framework that guides the contextual screening of feasible enforcement options. Overall, the findings demonstrate how citizen-sourced data and computational reasoning can support a more adaptive, participatory, and system-aware approach to sustainable traffic law reform.
Deploying heavy-duty electric trucks under real-world uncertainty is operationally challenging, particularly when multiple vehicles compete for limited public charging resources and face uncertain wait times. This research studies the Fixed-Route Vehicle Charging Problem and formulates it as a multistage stochastic program under charging congestion uncertainty. The delivery system is modeled as a discrete-event process triggered by physical route milestones, and charging congestion uncertainty is represented by a Markovian transition. To address the resulting mixed-integer structure, we apply stochastic dual dynamic programming (SDDP) as a tactical planning approach, while accommodating discrete vehicle dynamics within the convexity requirements. Computational experiments on a California logistics network showed that the proposed approach performed effectively across multiple geographically diverse delivery routes. Compared to a multistage stochastic integer programming benchmark, SDDP achieved a competitive solution quality while reducing training time as the number of route instances increased. Out-of-sample simulations further demonstrated that policies derived from SDDP remained robust under distributional shifts toward more congested scenarios. Overall, this study establishes SDDP as a tractable and scalable framework for generating high-quality operational policies for heavy-duty electric fleets under congestion uncertainty.
Many pavement design methods, including the widely adopted AASHTO 1993 empirical approach and most mechanistic-empirical (M-E) approaches, only account for seasonal variation of subgrade modulus throughout the pavement life and use either a single stiffness measure or a single set of seasonal values for the whole subgrade layer. This limits their reliability under changing moisture conditions caused by rising groundwater tables, sea-level rise, or other factors. This study introduces a methodology to integrate temporal and spatial variations in subgrade modulus caused by rising groundwater into existing empirical and M-E pavement design methods. The approach uses soil–water characteristic curve relationships to model moisture-dependent modulus changes and applies layered elastic analysis to derive equivalent modulus values. Predictive models were developed for all standard AASHTO subgrade soil classes, enabling more accurate pavement performance forecasts under varying groundwater conditions. To demonstrate application of this work, the predictive models were applied to predict pavement deterioration curves for empirical and M-E approaches that reflect evolving moisture conditions on pavement performance over time. The final output of this study is a set of predictive models that can be leveraged to improve pavement resilience by informing more effective decision-making, optimizing design strategies, and supporting proactive adaptation to changing moisture conditions.
This study introduces a systematic redesign of trip-based travel demand models to explicitly incorporate connected automated vehicles (CAVs). The framework models auto ownership of CAVs and human-driven households separately, accounts for zero-occupancy vehicle (ZOV) trips, and modifies trip distribution, mode choice, and temporal patterns to reflect anticipated behavioral and operational changes because of CAV presence. The framework is then applied to the Triangle Region of North Carolina, using Triangle Regional Model Generation 2, and reveals that CAV adoption increases vehicle miles traveled (VMT) and encourages longer trips, with average discretionary and work trips rising by 28%–31% under a 70% CAV adoption rate. Although VMT increases, effective capacity gains reduce total network delay by as much as 60% relative to the 2050 baseline. At the facility level, demand-to-capacity ratios decrease, indicating that some roadway expansion projects could potentially be deferred. Sensitivity analysis reveals that system performance is highly dependent on realized capacity improvements and cautious assumptions, producing nearly 40% more delay than the expected scenario. The proposed redesign framework provides transportation agencies with a scalable and practical approach to incorporating CAVs into long-range planning, project prioritization, and investment decision-making.
This conceptual and methodological synthesis examines why the location of the strongest station-area response is not stable across mature metro cities. Instead of estimating a pooled East Asian transit premium, it uses peer-reviewed evidence and official urban and rail indicators for Taipei, Seoul, and Tokyo to clarify how peak location depends on outcome type, project timing, spatial dependence, submarket structure, and station-front pedestrian integration. The Taipei evidence reviewed here is used to examine a temporal-displacement mechanism; the Seoul evidence reviewed here to examine a spatial-confounding mechanism; and the Tokyo evidence reviewed here to examine a core-reintegration mechanism. These are case-dominant mechanisms identified from the reviewed evidence, not exclusive labels for every station in each metropolitan system. The synthesis separates three layers: models estimated in the cited studies, transparent calculations performed in the present manuscript, and modeling templates proposed for future harmonized comparative research. It further specifies how vertical walkability can be measured through multilevel circulation, barrier-free access, entrance permeability, weather-protected continuity, transfer directness, and plaza legibility. The contribution is therefore to develop a comparative framework for identifying edge-centered, donut-shaped, model-sensitive, and re-centered station-area gradients, and for clarifying how different forms of evidence can inform the analysis of peak location across mature metro systems.
This study examines mismatches between Household Travel Survey (HTS) and mobile phone-based LTE/5G Cellular Signaling (LCS) data in representing origin–destination (OD) trip patterns in Seoul, South Korea. By comparing OD flows across subgroups defined by travel type, age group, gender, and arrival time, we quantify distributional mismatches using standardized root mean square error (SRMSE) and the relative zero-cell ratio. The results reveal larger mismatches for children, young adults, and noncommuting trips. A spatial regression analysis, using origin-level SRMSE as the dependent variable, shows that areas with larger numbers of households, higher average land prices, and larger tertiary sector business size exhibit lower mismatches, whereas areas with concentrated residential land use and high OD pattern diversity show higher mismatches. The findings suggest that observed mismatches between HTS and LCS are associated with subgroup characteristics and spatial context. These insights underscore the need for tailored data integration and sampling strategies that account for behavioral and spatial biases when combining survey and passively collected mobility data for travel behavior analysis.
This study introduces an innovative dynamic charging solution, defined as a platoon-based vehicle-to-vehicle charging (PV2VC). technology. A fleet of electricity suppliers (ESs) can be deployed to transfer power to other electric vehicles, defined as electricity requests (ERs), while moving in platoon to avoid the detour and delay at a charging station. We mathematically formulate a mixed integer linear programming (MILP) model for the PV2VC problem, along with two fundamental and benchmark operation scenarios with minor simplifications, the electric vehicle routing problem (EVRP) and electric vehicle platooning problem (EVPP). The objective is to minimize the total energy consumption and travel time for ERs, including the charging time at CS, platoon formation delay, and wait time for the PV2VC service. A set of numerical experiments with five scenarios are tested and the computational performance between the commercial software applied to the MILP model and the proposed genetic algorithm are compared on a modified Sioux Falls network. By comparison with the optimal benchmark scenario, the results show that the PV2VC technology can save up to 11.07% in energy consumption, 11.65% in travel time, and 11.26% in total cost. For the PV2VC operational scenario, it would be more beneficial for long-distance vehicle routes with low initial state of charge, sparse charging facilities, and where travel time is perceived to be higher than energy consumption costs.
This study investigated the feasibility of deriving deflection bowl parameters (DBPs), which are obtained from falling weight deflectometer measurements, directly from deflection slopes reported by the traffic speed deflectometer (TSD). Focus was placed on evaluating four popular DBPs, namely, (i) central deflection ( D 0 ); (ii) surface curvature index (SCI); (iii) base damage index (BDI); and (iv) base curvature index (BCI). To achieve this, one million pavement configurations were simulated in a layered elastic model to generate synthetic DBPs and corresponding TSD slopes. A multivariate linear regression model was formulated with synthetic TSD slopes as the independent variables and DBPs as the dependent variables. The model was shown to successfully estimate SCI, BDI, and BCI with acceptable error margins for pavement management applications. However, predictions for D 0 exhibited errors that were too high. As the analysis here was based on perfect data, these results should be considered the “best performers” in terms of what can be achieved with a linear model. Moreover, the study revealed that strong correlations are to be expected between TSD slopes reported by adjacent sensors. Subsequently, it was demonstrated how such correlations may be leveraged to estimate data quality and detect anomalies in field measurements.
The contact between concrete slabs and supporting base layers influences the mechanical and durability performance of pavement systems. This paper presents a new nondestructive evaluation (NDE) methodology for quantifying slab-support contact conditions using shear wave reflections. While current stress-wave NDE methods, such as impact-echo and falling weight deflection, infer the interfacial contact condition given knowledge of layer properties and thickness, the proposed method quantifies the interface contact condition without these initial inputs. The shear wave reflection ratio ( SWR z ) is defined as the amplitude ratio between successive shear wave echoes reflected from a slab-base interface. To isolate the interface contact from other losses, the signal amplitudes are corrected for geometric spreading and material attenuation. Laboratory experiments were conducted using a phased-array shear wave system on 76-mm-thick plexiglass and 76- and 100-mm-thick mortar slabs, supported by several materials (e.g., air, marbles, sand, and cement) and under multiple vertical pressure levels. The results demonstrated that the SWR Z values showed statistical significance ( p < 0.01) between support material type and increasing applied vertical pressure to the slab surface. As expected, the slab-air interface specimens exhibited the highest SWR z of approximately 1.0. Additionally, increasing the vertical pressure at the slab-sand interface reduced the SWR z values, indicating improved slab-base contact. The proposed methodology provides a reliable, NDE tool for detecting changes in slab-base contact conditions (i.e., kissing bond to complete separations or air gaps), which can be effective in evaluating concrete slab-base and concrete overlay contact conditions over time.
The peak deviation time (PDT) between subway stations and lines is a critical temporal misalignment in urban rail systems, yet it remains understudied. Accurately understanding PDT is essential for calibrating station-level peak-hour time (PHT) during planning, which directly informs station design ridership and facility configuration. Ignoring PDT can result in persistent supply–demand mismatches, exacerbating platform congestion and impairing operational efficiency. To address this gap, this study develops a spatial econometric framework to examine the spatiotemporal patterns and correlates of PDT. We implement a model selection procedure comparing the spatial lag model (SLM), spatial error model (SEM), and spatial Durbin model (SDM) with multiple network distance-based spatial weight matrices, enabling the characterization of spatial interdependencies among stations. Using multi-source data from the Xi’an subway system, the results, interpreted as conditional associations given the cross-sectional design, show that: (1) PDT exhibits significant spatiotemporal heterogeneity, with the SDM outperforming alternative models in capturing spatial autocorrelation; (2) the correlates of PDT are highly period- and direction-specific, with higher shares of recreational and medical land uses consistently associated with smaller temporal deviations across multiple peak periods, indicating their potential relevance as diagnostic indicators in station-area planning contexts; and (3) significant spatial spillover patterns are observed, with factors such as transport hub land ratio and terminal or transfer station attributes associated with both local and neighboring stations’ PDT. These findings underscore the importance of a network-wide perspective for station facility planning and operational management.
Pavement friction is vital for road safety, supporting effective steering, braking, and acceleration. Adequate friction levels help reduce the risk of skidding, yet few studies have explored their link to crash injury severity. This study utilizes high-resolution continuous pavement friction measurement (CPFM) data to examine how friction and other surface features influence crash injury outcomes. In 2023, CPFM data were collected over 2,000 lane miles in the Tampa Bay, Florida area. Friction (Sideway-force Coefficient Routine Investigation Machine [SCRIM] coefficient), macrotexture (mean profile depth [MPD]), and roughness (International Roughness Index [IRI]) were spatially matched to a 0.2-mi buffer around crash sites. A mixed logit model with heterogeneity in means and variances assessed the effects of pavement, roadway, traffic, and vehicle factors on crash injury severity. Findings reveal that the maximum three-point moving average of friction is the most suitable indicator for modelling injury severity. Higher pavement friction significantly reduces the likelihood of serious or fatal injuries. MPD values of 0.8–1.2 mm on dense-graded surfaces are associated with fewer slight injuries, while MPD values above 2 mm on open-graded surfaces increase the likelihood of severe injuries. Additional contributors include roughness, cracking, rutting, raveling, and pavement condition, though some exhibit non-unidirectional effects depending on context.
An intelligent pre-assembly framework integrating three-dimensional (3D) laser scanning with hierarchical point-cloud registration is presented to improve digital assembly accuracy of precast concrete T-beams. First, point cloud data of individual beam segments are acquired. Coarse registration is performed using principal component analysis (PCA) and refined through an enhanced Lie algebra-based iterative closest point (Lie-ICP) algorithm for high-precision alignment. In the Hezhou Expressway project, a discrepancy of less than 2.0 mm between the registered point cloud and total station measurements is observed. Following the fine registration, the residual error is further reduced to 1.83 mm, and the maximum assembly misalignment remains within 2.9 mm. Precision and reliability of 3D digital assembly for precast T-beams are effectively enhanced, providing a robust technical foundation for quality-controlled assembly of bridge precast components.
As demand for intermodal freight transportation in the United States continues to grow, it is essential to develop strategies that enhance the capacity and operational efficiency of transporting containers by rail. Self-Propelled Autonomous Railcars (SPARCs) are one promising technology to enhance rail/road intermodal rail service, and this study quantifies their potential terminal area, resource, performance, and energy benefits in comparison to conventional trains (CTs). An integrated analysis framework was developed that combines high-level infrastructure layout optimization with simulation of operational-level terminal congestion to evaluate terminal performance in terms of container processing time and energy consumption. Various output metrics were compared for SPARCs and CTs across different train/vehicle configurations and daily throughput volumes. The results indicated that SPARCs consistently outperformed CTs in both spatial and temporal efficiency. From a strategic planning perspective, typical CT lengths required nearly three times the yard footprint of the shortest SPARC platoons to process the same number of containers. From an operational perspective, SPARCs achieved shorter processing times for both inbound and outbound containers, experienced lower train delays, and required fewer resources for equivalent throughput. Delay function coefficients fit to power functions indicated that CTs were more sensitive to train frequency, leading to earlier system saturation under limited resources. Furthermore, to achieve comparable processing times, CTs required significantly more terminal hostlers than SPARCs. Based on these results, SPARCs offer greater flexibility and energy efficiency than CTs for low-volume short-haul corridors, making them a promising alternative to expand the network reach of future intermodal freight rail systems.
Rural public transit systems frequently face challenges in balancing operational efficiency and passenger convenience. Addressing the issue of transportation insecurity in rural areas, this study analyzes the total cost and effectiveness of public transit service in regions characterized by low demand density. Flexible-route services generally perform well in low-demand environments due to their adaptability, but they cannot accommodate long-distance trips required by passengers without access to personal vehicles. Conversely, traditional fixed-route buses are cost-effective along core corridors while suffering from limited coverage, forcing inconvenient transfers and extra travel times beyond the established network. Consequently, neither purely flexible nor purely fixed-route approaches effectively meet the transportation needs of low-density communities. To overcome these limitations, this paper proposes an integrated public transit framework formulated as a deterministic two-stage optimization model, combining flexible-route services with scheduled fixed-route operations to achieve seamless door-to-door connectivity. The framework explores controlled deviations of fixed-route buses with limited headway adjustments and coordinates flexible shuttles to effectively serve first- and last-mile segments. This integrated approach aims to minimize overall system operating costs while maintaining acceptable passenger waiting and in-vehicle travel times. Computational experiments and sensitivity analyses on synthetic rural networks demonstrate the scalability and effectiveness of the model in reducing combined vehicle-mileage and passenger-time costs.
Left-turn maneuvers are among the most hazardous intersection movements due to conflicts with oncoming vehicles and crossing pedestrians. Existing traffic standards rely mainly on vehicle volumes and collision history, overlooking how intersection geometry and driver seating position affect pedestrian visibility. Without protected left-turn phases, pedestrians remain especially vulnerable where A-pillar obstructions impair the driver’s line of sight. This study examines the relationship between intersection geometric configurations and driver eye positions and their effect on pedestrian visibility losses caused by A-pillar occlusion during left turns. A microscopic simulation framework was developed using 3D point clouds from 198 left-turn movements in Edmonton, Alberta, integrating 25 driver eye positions per movement to capture realistic seating variability. Ray-tracing methods were applied to quantify A-pillar occlusion and regions outside the driver’s field of view. Occlusion metrics were analyzed in relation to the geometric features, including the lateral distance to the left-turning vehicle, crossing width, and pedestrian crossing length. Results revealed that larger adjacent widths reduced maximum A-pillar blind zones but increased average occlusion, while longer crosswalks decreased average A-pillar occlusions but expanded the area outside the driver’s field of view. Driver eye position significantly influenced occlusion, with positions farther back reducing A-pillar obstruction and positions forward and upward increasing it, resulting in approximately 11% change in average occlusion and 21% in maximum occlusion. These findings underscore the influence of intersection geometry and driver seating position on pedestrian visibility and offer a data-driven foundation for proactive intersection design, especially where volume-based criteria may overlook safety risks.
Traffic microsimulation routinely generates second-by-second speed trajectories that contain nonphysical fluctuations in acceleration and jerk, which can lead to systematic overestimation of vehicular emissions. This study presents Trajectory Refinement for Emission Modeling (TRIM), a platform-independent post-processing framework that refines microsimulated trajectories through physics-based optimization. By enforcing acceleration-dependent jerk constraints, car-following safety constraints, and trajectory-consistency within a mixed-integer quadratic programming formulation, TRIM transforms raw simulation outputs into physically plausible, emission-consistent trajectories. Validated against high-resolution unmanned aerial vehicle-derived ground truth under three car-following models (Wiedemann 99, Krauss, and the Intelligent Driver Model), TRIM reduces total emission errors within 9% for nitrogen oxides and 7% for PM 2.5 , with spatial errors predominantly within ±20% (±10% midblock). The platform-independent framework provides a transferable pathway for standardized emission assessment across simulation platforms, with an open-source implementation publicly available.
This study introduces a laboratory testing framework for estimating the performance grade (PG) of asphalt binders in reclaimed asphalt pavement (RAP) without using solvent extraction and recovery. The study was conducted in two phases: Phase I aimed to identify suitable performance tests for assessing RAP binder stiffness using laboratory-prepared artificial RAP, and Phase II focused on validating these selected tests for estimating binder PG of field RAP. In Phase I, the indirect tensile asphalt cracking test (IDEAL-CT), high-temperature indirect tensile test (HT-IDT), and Dongre workability test were conducted on artificial RAP samples prepared with different asphalt binders and laboratory aging conditions. Among these, the IDEAL-CT and HT-IDT exhibited a strong exponential correlation with the extracted RAP binder PG and were subsequently evaluated in Phase II for their feasibility and accuracy in estimating binder PG for three distinct field RAP sources using the proposed testing framework. The framework required the preparation of three re-mixed RAP samples for each source, prepared by mixing the post-ignition aggregates with three asphalt binders of known PG at the same asphalt content as the field sample. All the re-mixed and field samples underwent IDEAL-CT and HT-IDT testing, with results analyzed to determine the estimated PG of the field RAP binder. Using indirect tensile test (IDT) strength from HT-IDT and peak load (P max ) from IDEAL-CT, the framework produced reliable PG estimations for two out of three field RAP sources, highlighting its promise as a non-hazardous alternative to solvent extraction and recovery for RAP binder quality characterization.
Shared modes of micromobility play an increasingly important role in urban transportation. Although mode choices on bike-sharing systems (BSS), shared electric scooters (ES) and short-distance public transit (PT) have been examined in recent years, there is a lack of joint analyses, especially considering latent choice influences such as environmental consciousness. To address this gap in knowledge, we estimate a hybrid choice model (HCM) with a nested mixed multinomial logit choice component for the mode choice between BSS, ES, and PT that fits the data with an adjusted rho-squared value of 0.45 and results in plausible parameter estimates. As a result of our joint consideration of BSS, ES, and PT, we find that respondents with a higher environmental consciousness are more likely to use BSS and PT than ES, even though all three modes are often considered as being used by travelers with a high environmental awareness. Additionally, we find that mode choice is nested in sheltered and unsheltered modes with PT in the sheltered nest and BSS and ES in the unsheltered nest. Furthermore, we find that smartphone or bag holders may not increase the utility for BSS and ES and that the remaining ES battery capacity does not appear to have a large effect on ES choice.
