Search Results (143)

The Korean government proposes introducing a transfer-type bus system to reduce urban congestion. Transfer-type buses turn around at the Seoul border, requiring passengers to transfer to other modes to reach downtown. These buses have shorter routes, allowing reduced headways and increased bus supply. While this approach reduces congestion in the downtown area, it may cause transfer resistance, making it essential to analyze willingness to shift (WTS). This study uses a latent class model to categorize potential interregional bus users into three types: transfer avoidance, cost-sensitive, and time-sensitive. Over 50% of users in each group express WTS, showing a positive response to the transfer-type bus introduction. The choice model results indicate that the travel time and cost of direct type buses affect WTS, suggesting that policies should consider these factors for effective implementation. Full article

With the increase in urban–rural disparities in China, rural public transportation systems in low-density areas face unique challenges, especially in the contexts of sparse population, complex topography, and uneven resource allocation; research on public transportation in low-density areas has had less attention compared to high-density urban areas. Therefore, how to solve the dilemma of public transportation service provision in low-density rural areas due to sparse population and long travel distances has become an urgent problem. In this paper, a dynamic optimization model based on a two-layer planning framework was constructed. The upper layer optimized the topology of multimodal transportation nodes through the Floyd shortest path algorithm to generate a composite network of trunk roads and feeder routes; the lower layer adopted an improved Logit discrete choice model, integrating the heterogeneous utility parameters, such as time cost, economic cost, and comfort, to simulate and realize the equilibrium allocation of stochastic users. It was found that the dynamic game mechanism based on the “path optimization–fairness measurement” can optimize the travel time, mode, route, and bus stop selection of rural residents. At the same time, the mechanism can realize the fair distribution of rural transportation network subjects (people–vehicles–roads). This provides a dynamic, multi-scenario macro policy reference basis for the optimization of a rural transportation network layout. Full article

This study investigates the factors influencing the energy consumption and reliability of haul trucks in open-pit mines and quarries, where fuel costs and the environmental impact are significant. Traditional analysis of haulage systems often overlooks crucial aspects such as energy efficiency in the specific mining environment and the effect of road configurations on truck performance. As sustainability becomes increasingly important, reducing fuel consumption not only reduces costs but also reduces greenhouse gas emissions. A key focus of the study is the link between haul truck reliability and overall efficiency. Frequent breakdowns increase maintenance costs, lead to unplanned downtime, and increase fuel consumption, all of which have an impact on the environment. Reliable transport systems, on the other hand, improve efficiency, reduce costs, and support sustainability goals. The authors analyze the energy consumption of trucks in relation to vehicle performance parameters and transport route characteristics. Discrete modeling of the transport system showed the impact of the operating environment on the variability of energy consumption and vehicle reliability. The study highlights the importance of understanding specific energy consumption in order to optimize the choice of transport system, as transport costs are a major cost of resource extraction. By analyzing the effect of road quality on vehicle performance, the authors suggest that improvements to the road surface can more easily improve vehicle reliability and energy intensity than changes to other road design elements. The study presents a quantitative analysis of the impact of haul road conditions on the operational efficiency of haul trucks in mining environments. Through discrete simulation models, two scenarios were analyzed. Total operational time decreased by 11.2% when road quality improved, demonstrating the critical role of surface maintenance. Additionally, breakdown times were reduced by 44%, maintenance by 15%, and empty travel by 9% in the optimized scenario. These findings underscore the necessity of maintaining optimal road conditions to prevent substantial efficiency losses and increased maintenance costs. Full article

Empirical studies have suggested that travelers’ risk attitudes affect their choice behavior when travel conditions are stochastic. By considering the travelers’ risk attitudes, we extend the classical two-route model, in which road capacities vary due to such shocks as bad weather, accidents, and special events. Two information regimes have been investigated. In the zero-information regime, we postulate that travelers acquire the variability in route travel time based on past experiences and choose the route to minimize the travel time budget. In the full-information regime, travelers have pre-trip information of the road capacities and thus choose the route to minimize the travel time. User equilibrium states of the two regimes have been analyzed, based on the canonical BPR travel time function with power coefficient $p$. In the special case $p=1$, the closed form solutions have been derived. Three cases and eleven subcases have been classified concerning the dependence of expected total travel times on the risk attitude in the zero-information regime. In the general condition $p>0$, although we are not able to derive the closed form solutions, we proved that the results are qualitatively unchanged. We have studied the benefit gains/losses by shifting from the zero-information to the full-information regime. The circumstance under which pre-trip information is beneficial has been identified. A numerical analysis is conducted to further illustrate the theoretical findings. Full article

Travel time is one of the most important pieces of information that the Dynamic Message Sign (DMS) provides to drivers. However, several variables, including traffic-related factors and DMS message characteristics, might impact the accuracy of the travel time when the DMS is used to display the travel time for alternate routes. Therefore, this study aims to look at the variables that affect the route choice’s displayed travel time accuracy as it relates to the individual driver. The accuracy of the travel time displayed on a DMS on I-75 in Saginaw, Michigan, was examined using logistic regression analysis. The results suggest that for effective traffic management for high traffic demand times (peak hour and day of the week), avoiding using travel time information displayed with other types of messages at the same time (phasing) and adapting a message update time between 2 to 3 min can improve the DMS travel time information accuracy. Practitioners and planners can use the findings to improve driver compliance with the DMS message that is being displayed. Full article

The differential toll policy has emerged as an effective method for regulating expressway traffic flow and has positively impacted the efficiency of vehicular movement, as well as balanced the spatial and temporal distribution of the road network. However, the acceptance of differentiated charging policies and the range of rates associated with these policies warrant further investigation. This study employs both revealed preference (RP) and stated preference (SP) survey methods to assess users’ willingness to accept the current differentiated toll scheme and to analyze the proportion of users opting for alternative travel routes and their behavioral characteristics in simulated scenarios. Additionally, we construct a Structural Equation Model-Latent Class Logistics (SEM-LCL) to explore the mechanisms influencing differentiated toll road alternative travel choices while considering user heterogeneity. The findings indicate that different tolling strategies and discount rates attract users variably. The existing differentiated tolling scheme—based on road sections, time periods, and payment methods—significantly affects users’ choices of alternative routes, with the impact of tolling based on vehicle type being especially pronounced for large trucks. The user population is heterogeneous and can be categorized into three distinct groups: rate-sensitive, information-promoting, and conservative-rejecting. Furthermore, the willingness to consider alternative travel routes is significantly influenced by factors such as gender, age, driving experience, vehicle type, travel time, travel distance, payment method, and past differential toll experiences. The results of this study provide valuable insights for highway managers to establish optimal toll rates and implement dynamic flow regulation strategies while also guiding users in selecting appropriate driving routes. Full article

With the continuous advancement of Mobility as a Service (MaaS), a hybrid traffic flow comprising MaaS-based and conventional trips has emerged within transportation networks, leading to diverse behaviors among heterogeneous travelers. Given the coexistence of heterogeneous travelers during the promotion of MaaS, this paper investigates two distinct groups: travelers using MaaS subscription services (defined as “subscribed users”) and traditional travelers who rely on personal experience (defined as “decentralized users”). Accordingly, we propose a day-to-day and within-day bi-level dynamic traffic assignment model for heterogeneous travelers under the MaaS framework. By optimizing subscribed users’ travel decisions, this model assists urban planners in predicting the evolution of mixed traffic flows, enabling improved road resource allocation and subscription service mechanisms. For the day-to-day component, the model explicitly incorporates mode-switching behaviors among heterogeneous travelers. In the within-day context, departure time and route choices are considered, along with travel time costs and additional costs arising from early or late arrivals. Consequently, we propose a within-day, time-dependent traffic assignment model specifically tailored for heterogeneous users. For modeling subscribed users’ traffic assignment, we develop a system-optimal (SO) bi-level programming model aiming at minimizing the total travel cost. Furthermore, by integrating an improved Genetic Algorithm with the Method of Successive Averages (MSA), we introduce an enhanced IGA-MSA hybrid algorithm to solve the proposed model. Finally, numerical experiments based on the Nguyen–Dupuis network are conducted to evaluate the performance of the proposed model and algorithm. The results indicate that the network with heterogeneous MaaS users can reach a steady state effectively, significantly reducing overall travel costs. Notably, decentralized users rapidly shift towards becoming subscribed users, highlighting the attractiveness of MaaS platforms in terms of cost reduction and enhanced travel experience. Additionally, the IGA-MSA hybrid algorithm effectively decreases overall travel costs in the early evolution stages and achieves a more balanced temporal distribution of trips across the system, effectively managing congestion during peak periods. Full article

While extensive research has examined how major life events affect travel habits, less attention has been paid to the impact of minor environmental changes on commuting behavior, particularly regarding shared autonomous vehicles (SAVs). This study investigated how daily disruptions and incremental environmental changes influence commuter behavior patterns and SAV adoption in Shanghai, applying the theory of interpersonal behavior framework. The study surveyed 517 Shanghai residents, examining travel satisfaction, commuting habits, psychological factors (such as habit strength and satisfaction), and attitudes towards SAVs. Structural equation modeling was employed to test hypotheses about psychological factors influencing SAV adoption, while logistic regression analyzed how these factors affected mode choice across different disruption contexts. Analysis revealed that psychological factors, particularly habit and satisfaction, were stronger predictors of SAV adoption than attitude-based factors. Route obstructions and workplace relocations significantly increased SAV consideration. Even minor, recurring disruptions, such as construction zones, showed strong effects on commuting behavior, supporting the habit discontinuity hypothesis and emphasizing the importance of minor disruptions in driving behavioral change. The study extends the theory of interpersonal behavior by integrating habit discontinuity theory to explain how minor disruptions drive SAV adoption. This research provides actionable insights for urban planners and policymakers, recommending that SAV trials and targeted interventions be implemented during infrastructure changes or other commuting disruptions to promote SAV adoption and foster more sustainable transportation systems. Full article

The accurate reconstruction of vehicle paths is essential for effective highway toll management. To address the challenge of multiple possible paths due to missing trajectory data, this study proposes a novel two-stage model for vehicle path reconstruction. In the first stage, a Gaussian Mixture Model (GMM) is integrated into a path choice model to estimate the mean and standard deviation of travel times for each road segment, utilizing an improved Expectation Maximization (EM) algorithm. In the second stage, based on the estimated time parameters, path choice prior probabilities and observed data are combined using maximum likelihood estimation to infer the most probable paths among candidate routes. The results indicate that the improved EM algorithm achieved convergence in 17 iterations compared to 41 iterations for the traditional EM algorithm. The two-stage model outperforms the Shortest Path and Bidirectional Long Short-Term Memory models in path reconstruction, particularly with a high number of missing trajectory points. Additionally, when the number of candidate paths $K=4$, the path reconstruction performance is optimal. These results demonstrate the effectiveness of the proposed method in handling sparse and incomplete trajectory data, offering robust and accurate vehicle path estimations that enhance traffic management and toll calculation precision. Full article

Due to its anticipated impacts on the performance of transportation systems, intelligent transport systems (ITS) have emerged as one of the most extensively investigated topics. The U.S. Department of Transportation has defined route guidance systems (RGSs) as one of the main categories within ITS. Systems like these are essential components when managing travel and transportation. While RGSs play a pivotal role in both present and future transportation, there has been limited research on evaluating the effectiveness and dependability of integrating them with vehicular communication frameworks. Therefore, this paper aims to evaluate the RGS architectures proposed to date in the literature, providing comparisons and classifications based on their structures and requirements for communication systems. Moreover, it explores existing, next generation, as well as prospective choices for V2X communication technologies, evaluating how well they contribute to the development of RGS applications by integrating them with potential communication systems. Specifically, this study assesses the suitability of communication technologies in meeting the requirements of RGS applications. In conclusion, it suggests a framework for integrating RGS and V2X systems and offers directions for future research in this area. Full article

With the development of smart cities and intelligent transportation systems, path planning in multi-scenario urban mobility has become increasingly complex. Traditional path-planning approaches typically focus on a single optimization objective, limiting their applicability in complex urban traffic systems. This paper proposes a multi-objective vehicle path-planning approach tailored for diverse scenarios, addressing multi-objective optimization challenges within complex road networks. The proposed method simultaneously considers multiple objectives, including total distance, congestion distance, travel time, energy consumption, and safety, and incorporates a dynamic weight-adjustment mechanism. This allows the algorithm to provide optimal route choices across four application scenarios: urban commuting; energy-efficient driving; holiday travel; and nighttime travel. Experimental results indicate that the proposed multi-objective planning algorithm outperforms traditional single-objective algorithms by effectively meeting user demands in various scenarios, offering an efficient solution to multi-objective optimization challenges in diverse environments. Full article

This research aims to optimize corridors in Hyderabad, Sindh, to promote modal choice, reduce congestion, and enhance livability. This study focused on developing and evaluating multimodal wide corridor routing methods, analyzing the modal choice behavior of travelers using a generalized cost model and a mixed constant and separate user balance model, and implementing and assessing innovative road space management strategies. The data were collected using GIS (Geographical Information System) to compare the performance and impacts of the proposed methods and techniques with existing ones, such as shortest path, minimum interference, maximum capacity, and lane addition, using various performance measures, such as travel time, modal share, congestion level, environmental impact, safety, and equity. This research aims to optimize corridors in Hyderabad, Sindh, to encourage various transportation options, such as the BRT system and Peoples Bus Service, to reduce congestion and enhance livability by developing and accessing different methods and strategies. This study analyzed available data through a geospatial perspective to optimize corridors in Hyderabad, Sindh, focusing on multimodal routing methods, modal choice behavior, and innovative road space management strategies to enhance urban livability rather than relying on simulation software or field-collected data. Full article

Gastronomic tourism has become a dynamic segment within the tourism sector, serving as a vital tool for the economic, cultural, and environmental growth of regions that emphasize culinary experiences. This study examines the motivations of young food-motivated tourists (“foodies”) and their impact on travel choices and destination development. Using a quantitative methodology, data were collected from 170 respondents (131 valid answers) in Portugal. The data were collected through the PTFoodies survey, a tool specifically designed by the authors to analyze tourist motivations and their connections to destination attributes. Principal Component Analysis (PCA) identified five key factors—endogenous products, local gastronomy, quality and innovation, authenticity and tradition, and diversity—revealing a balance between sustainability, local authenticity, and openness to innovation in tourist preferences. Additionally, correlation analysis highlighted significant relationships between motivations, such as the strong association between an interest in culinary routes and event participation and between local product appreciation and the preference for organic food. These findings contribute to both theory and practice by refining motivational theories specific to gastronomic tourism, offering insights into destination development strategies and emphasizing sustainable practices that resonate with tourists’ preferences. The study provides destination managers with actionable strategies to attract food-motivated tourists and foster sustainable community growth through gastronomic tourism. Full article

This paper proposes a decision-making framework for a multiple-period planning of electric vehicle (EV) charging station development. In this proposed framework, transportation planners seek to implement a phased provision of electric charging stations as well as repurposing gas stations at selected locations. The developed framework is presented as a bi-level optimization problem that determines the optimal electric charging network design while capturing the practical constraints and travelers’ decisions. The upper level minimizes overall vehicle CO emissions by selecting optimal charging stations and their capacities, while the lower-level models travelers’ choices of vehicle class (EV or conventional) and travel routes. A genetic algorithm is developed to solve this problem. The results of the numerical experiments describe the sensitive nature of EV market penetration rates in the urban traffic stream and overall vehicle CO emissions to EV charging station availability and capacity. The findings can assist transportation agencies in designing effective EV charging infrastructure by identifying optimal locations and capacities, as well as in creating policies to encourage EV use over time. This study supports broader efforts to reduce air pollution and promote sustainable transportation by promoting EV adoption in the long term. Full article

The Hokuriku Shinkansen is among the five main Shinkansen lines in Japan. It starts from the capital, Tokyo, and passes through the Hokuriku region (Nagano City, Toyama City, and Kanazawa City) before reaching the Kansai region (Kyoto City and Osaka City). In Japan, the Hokuriku Shinkansen is being developed to increase the number of visitors in the area by shortening travel times and ensuring transportation networks in the event of a disaster; the Kanazawa–Tsuruga extension was opened on Saturday, 16 March 2024. The shortened travel times with this opening are expected to increase the number of visitors to the Hokuriku region and other regions, such as the Tokyo metropolitan area. The Tsuruga extension opening will connect the Hokuriku region with the Tokyo metropolitan area through direct service; however, travel between the Kansai and Chukyo regions will require transfers at Tsuruga Station, which would reduce convenience. Consequently, the flow of people in the Hokuriku region is expected to change significantly after spring 2024. Therefore, this study conducted a questionnaire survey of Kanazawa residents in Ishikawa Prefecture prior to the opening of the Tsuruga extension to analyze its effect on the choice of modes of transportation for travel to the Kansai region. The Hokuriku Shinkansen was found to be the most popular route for travel to the Kansai region after the opening of the Tsuruga extension, and the Hokuriku Shinkansen users prioritized comfort during travel and shorter travel times. Moreover, a high proportion of Hokuriku Shinkansen users were unemployed and received pensions, whereas a high proportion of private automobile users were students, housewives, or other people with no income. Full article