Smooth and efficient traffic flows are disrupted by bottlenecks, causing additional oscillations and delays. This paper proposes Dynamic Real-Time Trajectory Smoothing Algorithm (DRTSA), a generalizable trajectory optimization framework for traffic coordination under various bottleneck scenarios. DRTSA simultaneously optimizes efficiency by scheduling the spot-arrival time and speed of each vehicle to maximize throughput and improve sustainability simultaneously by minimizing speed variation to reduce oscillations. DRTSA generates piecewise optimal accelerations as advisory controls, while accommodating mixed traffic with human-driven vehicles under varying congestion levels. We evaluate DRTSA through simulations of two representative bottleneck types: a slow-moving vehicle creating a non-recurrent bottleneck scenario, and a signalized intersection (as a recurrent bottleneck scenario), in various traffic densities, CAV market penetrations, vehicle behaviors, and control ranges. Results show substantial improvements in flow rate, average speed, speed stability, and fuel efficiency, demonstrating that DRTSA can effectively enhance both mobility and sustainability in general bottleneck conditions.
Mass Rapid Transit is a globally efficient transport mode, yet its stations host large-scale pedestrian activity, leaving them vulnerable to hazards that can trigger evacuations. This study develops a pedestrian evacuation microsimulation framework capturing behavior across varying panic levels during metro station evacuations. Latin Hypercube Sampling and Random Forest modeling generate pedestrian behavior parameter combinations for calibrating the model and simulating panic conditions. Calibration uses a Genetic Algorithm, with validation via Geoffrey E. Havers statistics from CCTV data. Results show average speed fluctuates under low panic but remains stable under medium and high panic. Under low panic, most pedestrians evacuate in the later half; medium panic spreads evacuations evenly; high panic peaks in the first and last thirds. At peak hour (540 people), clearance times were 5.25-6.47 min, 5.12-5.25 min, and 4.90-5.07 min for low, medium, and high panic, respectively. These findings support countermeasures for safe, efficient MRT evacuations.
Rail transit station areas function as high-traffic nodes that merge mobility with commerce, services, and public life. However, existing research disproportionately emphasises traditional public spaces such as streets and parks, overlooking station areas as critical loci of activity. Using the GTWR model and hourly Baidu Heatmap data, this study investigates how urban characteristics shape vitality in Tianjin's rail station areas, capturing fine-scale spatiotemporal variations in human activity. Findings show that centrally located, commercially concentrated stations exhibit heightened vitality, typically following a bimodal daily rhythm. Variables such as public service density, floor area ratio, and bus route integration exert significant yet location-sensitive impacts. By combining spatiotemporal modelling with real-time population proxies, this research contributes an adaptable methodology and practical guidance for revitalising transit-adjacent urban areas. The findings provide practical insights to improve the vitality and efficiency of station-adjacent urban environments.
This study explores the impact that transportation infrastructure, land-uses, meteorological conditions and socio-demographics play on the production of shared, dockless E-Scooter trips in the City of Calgary. Using trip count extracted from the City of Calgary's E-Scooter pilot from 2019 to 2022, the study looks atthe impact of these characteristics by running a Zonal-Based Negative Binomial-Spatial Linear Feedback (NB-SLFM) Trip Generation for each year of the pilot period. Findings convey that land-use characteristics and the presence of active transportation infrastructure consistently influenced trip production yet, the significance of points of interest and socio-demographic variables varied. Since the pilot period extends before and after 2020, trip production may have partially been influenced by travel restrictions during the global COVID-19 pandemic. The SLFM added increased efficiency to the trip generation model and highlighted that the presence of spatial interactions stimulating trip production in neighboring locations.
Motorcycle-related crashes represent a major public health concern in Thailand, where motorcyclists account for approximately 74% of road traffic fatalities, highlighting their increased vulnerability due to interactions with larger vehicles. To address this issue, exclusive motorcycle lanes (EMLs) have been proposed as a potential solution. Therefore, this study aims to investigate the psychological factors influencing riders' intentions to use EMLs. The study gathered data from 951 motorcycle riders across three main regions of Thailand. The Structural Equation Model results revealed a significant association between the Health Belief Model and riders' intentions to utilize EMLs for reducing the risk of road crashes. The results indicate that perceived susceptibility is the most influential determinant across all regions, while perceived barriers exhibit context-dependent effects, particularly in industrial areas. Regional differences highlight the importance of tailoring infrastructure and policy strategies to local traffic environments and offer insights for relevant authorities for enhancing safety measures.
The rapid development of China's high-speed railways (HSRs) and the reform of railway market revenue have promoted the formation and improvement of the bilateral railway passenger market. This paper explores the travel perception psychology of HSR passengers at different distances based on travel satisfaction. First, based on the passenger travel data of the Nanning-Guangzhou (NG) HSR line, the K-means clustering method is used to divide passengers into three categories: short-distance, medium-distance and long-distance travel. Secondly, based on the HSR passenger satisfaction index (HSR-PSI) structural equation, the key driving factors of HSR passengers' perceived satisfaction are classified and quantified, and the heterogeneity of the perceived levels of satisfaction factors of passengers with different travel distances is analyzed in multiple groups. Finally, specific intervention measures for passengers with different travel distances are determined through Importance Performance Map analysis (IPMA), and targeted suggestions for increasing passengers' satisfaction with HSR travel are proposed.
To solve the social dilemma between buses and passenger cars in mandatory lane changing for bus exiting, the evolutionary game is utilized to analyze the interactions between them. Considering the similarity and randomness in decision-making, the replicator dynamic and stochastic evolutionary game models are formulated, respectively. Numerical simulation results indicate that the cooperative tendencies of buses are more obvious than those of passenger cars. The Nash equilibrium of changing lanes and giving way is promoted more effectively by reducing time losses of buses or increasing safety benefits of passenger cars associated with the strategy of cooperation. Specifically, reducing the arrival deceleration of passenger cars by 0.35 m/s & sup2; and decreasing the time loss of buses by 1.91 s stimulates the safe and efficient lane changing. The models proposed in this paper provide decent theoretical and practical support for intelligent decision-making in urban public transportation.
This study focuses on the integrated reliability optimization of multimodal dangerous goods transportation, in which transport risk and routing cost are quantitatively evaluated. Given the higher potential risks associated with transfer nodes and multimodal routing schemes compared with single-mode transportation, a bilevel programming (BP) model is proposed to optimize both multimodal routing and mode-jointing at transfer nodes. The upper-level objective is to minimize the total risk, including route risk and risk equity. The lower-level model aims to optimize transport routes subject to multimodal cost constraints. For a small-scale case, we devise a tailored branch-and-bound algorithm using a depth-first-search strategy to derive an exact solution. As large-scale cases significantly increase the computational burden, a recoding-based simulated annealing algorithm is developed to obtain near-optimal solutions for the proposed BP model. The results show that multimodal transport schemes reduce route risk, risk equity, and total cost by 11.86%, 51.45%, and 22.94%, respectively.
The sensory dimension of pedestrian experience remains largely overlooked in pedestrian level of service (PLOS) studies. Moreover, existing PLOS models fail to fully capture pedestrian perceptions. The approaches used to model responses to street environments tend to be subjective in the selection and weighting of environmental variables. This paper aims to integrate pedestrian sensory experience into the PLOS evaluation through an objective approach. It builds on the findings of experimental cognitive-environmental studies to identify the variables that influence pedestrians' emotional states. The proposed approach postulates that each street possesses unique characteristics that affect pedestrians' emotions differently. This study proposes a PLOS model tailored to commercial streets, utilizing stepwise regression, and focuses on the commercial streets of Algiers, Algeria. The results show that the fa & ccedil;ade transparency, outdoor dining, sidewalk width, and vehicle volume impact the PLOS. The model can optimize the infrastructure design of commercial streets for more psychologically friendly experiences.
Transit-oriented development (TOD) is regarded as an effective approach for addressing urban sprawl and enhancing urban resilience particularly by supporting the revitalisation of fragile urban areas. From a perceived geographical perspective, this study examines the determinants of travel-related well-being (TRW) and their spatial effects to inform improvements in TOD resilience. Using 1,298 household surveys in Hangzhou, this study applies binary logistic regression, geographically weighted logistic regression, and linear mixed-effects models to examine spatial heterogeneity and temporal dynamics. The results indicate that gender, marital status, housing tenure, vehicle ownership, metro accessibility to workplaces, the quality of the cycling environment, and station accessibility significantly influence TRW, and that these relationships vary across space. The opening of metro stations is associated with improvements in residents' perceived travel-related resilience. By linking TOD resilience with perceived TRW, this study provides spatially and temporally nuanced evidence to inform resilient TOD planning in socio-spatially fragile urban areas.
This study explores the institutional factors influencing electric bus adoption across 23 countries from 2010 to 2023. Utilizing a newly developed panel dataset, the research integrates and analyses data to identify key trends. Principal Component Analysis is applied to construct indices based on Scott's institutional pillars, including Regulative, Technological Normative, Social Normative, Business Normative, and Cultural-Cognitive. Estimations are conducted using static econometric models, including fixed effects, random effects, and pooled Ordinary Least Squares, with robustness checks through specification tests and heterogeneity analysis, categorizing countries by median carbon emissions. Findings indicate that Technological, Social, and Business Normative factors significantly enhance electric bus production and sales, while Regulative factors negatively impact adoption, emphasizing the need for targeted electric bus policies. Cultural-Cognitive influences, though significant, present mixed effects, highlighting the need for further variable-specific investigation. This study offers valuable insights for policymakers and stakeholders, supporting the transition toward sustainable urban transportation.
Efficient traffic control is critical for urban arterial roads with mixed connected, automated and human-driven vehicles. Most studies optimize vehicle trajectories and signal control separately, lacking a dynamic integrated framework. This study proposes a closed-loop collaborative optimization framework combining BEV-based trajectory prediction, speed guidance and arterial signal coordination. A CNN-LSTM-attention model is developed for trajectory prediction to capture spatiotemporal vehicle interactions. Predicted trajectories guide vehicle speed and optimize arterial signal phase differences and timings via a genetic algorithm, forming a closed-loop through iterative strategy updates. SUMO simulations show the framework effectively reduces vehicle delay and improves traffic flow efficiency under mixed traffic conditions.
The unprecedented spike in food delivery demand has motivated the development of technology-based sustainable delivery modes such as Sidewalk Autonomous Delivery Robots (SADRs). Although there have been a number of studies to investigate the adoption of SADRs for food deliveries from the receiver perspective, a significant gap persists in comprehending the viewpoint of shippers (i.e. restaurants), who are the primary decision-maker to adopt SADRs. To investigate the adoption behaviors from the shipper perspective, this research conducted a survey on restaurant owners in South Korea to examine various restaurant - and owner-related factors affecting (1) the adoption choice and (2) the degree of replacement by developing an ordered probit model with sample selection. As a result, age, the current availability of delivery services, and attitudinal factors (Pro-SADR, awareness, and Anti-SADR) played a significant role in adoption behavior. Several insights to promote advanced delivery modes were derived and discussed based on the results.
Because U.S. public airports must balance social welfare objectives with financial sustainability, aeronautical pricing decisions play a central strategic role. The regulatory exemptions, notably those granted to grandfathered airports, may inadvertently prioritize specific segments, leading to anti-competitive behavior and price deviation. The ongoing debate surrounding the potential repeal of grandfather exemptions underscores the complex legal and financial implications. To delve into the impact of this exemption, we employed a spatial pricing frontier model. The results reveal a consistent industry-wide pattern, with an observed average overcharge of 9.9%. This finding serves as a revealing indicator of a systematic trend driven by underlying factors elucidated in this study. Grandfathered airports strategically engage in overcharging practices to generate a revenue surplus. Overall, the results suggest a reevaluation of grandfather exemptions and a balance between revenue generation and consumer welfare.
Urban areas in developing countries face severe mobility challenges, including congestion, overburdened public transit, and high emissions. This study presents a planning-oriented approach combining public policy and computational optimization to improve urban mobility. Specifically, it proposes a legal mandate requiring large companies to provide chartered bus services for employees, supported by an optimized routing algorithm designed to minimize operational costs. To evaluate this policy, we developed a routing algorithm based on the Rank-Based Ant System (RAS) metaheuristic. A case study was conducted with a major Brazilian mining company, comparing the algorithm's performance against their current manual planning. The results demonstrate potential monthly cost savings exceeding USD 50,000.00 and annual reductions of over 1.5 million kg in CO2 emissions. Furthermore, a scenario analysis simulating five companies illustrates broader improvements in traffic conditions and public transit quality, proving that this policy effectively supports urban mobility planning with minimal financial burden on municipalities.
Air pollution is one of the major environmental issues worldwide. Individuals can contribute by adopting eco-friendly practices, such as reducing energy consumption, using public transportation, and adopting electric cars (EC). However, the adoption of EC has been a challenge, and this study investigates the role of consideration of future consequences (CFC) in shaping consumers' intention to purchase. It examines how CFC influences key attributes such as problem awareness, outcome efficacy, personal norms, and consumers' attitudes towards EC, using a sample of 491 individuals and structural equation modelling. The findings reveal a significant and positive relationship between CFC and problem awareness, and it also positively influences outcome efficacy, personal norms and attitude. The study provides insights for marketers and policymakers, emphasising the importance of leveraging consumers' future-oriented thinking to accelerate the adoption of EC and thereby mitigate the environmental risks associated with air pollution.
