Transportation Research Interdisciplinary Perspectives最新文献

This study examines the impact of online food delivery on CO₂ emissions and transport intensity in Jakarta, Indonesia, by integrating data from both delivery drivers and customers. A total of 235 delivery instances were analyzed to discern typical distances and patterns inherent in these services. Additionally, insights into the demand side were obtained through customer surveys, yielding 203 valid responses, that included waiting times, costs, and behaviors related to alternative meal acquisition methods. CO₂ emissions generated by delivery processes were estimated, utilizing travel distances recorded by drivers. The traffic impact was assessed by calculating the road occupancy of motorcycles used for deliveries. Furthermore, the assessment of CO₂ emissions and transport intensity considered hypothetical emission scenarios without such delivery services based on customer indications of their alternative means of meal acquisition. This analysis revealed that frequent short journeys by delivery motorcycles significantly contribute to urban traffic congestion and CO₂ emissions, highlighting the environmental and traffic pressures resulting from the rise of food delivery services. Results from the customer survey revealed a profound dependence on these services, indicating that their availability prompts a significant number of additional trips, thereby exacerbating these impacts. The findings suggest recommendations for mitigating these adverse effects, including promoting the use of electric motorcycles for deliveries and managing demand by reassessing delivery fees to reflect the actual environmental costs.

Daily pedestrian travels involve navigating various locations, putting them at risk because they are vulnerable worldwide to road users. Road traffic accidents claim the lives of many pedestrians every year. Any roadway and traffic management design must consider the speed at which pedestrians cross or walk. To improve current operating policies and to provide logical safety assessments, it is essential to comprehend the characteristics of pedestrian speed. While crossing several vehicular lanes, pedestrians must look for vehicle gaps in each route according to the direction of traffic. However, existing studies often overlook the diversity in pedestrian behaviors. This paper seeks to summarize the pedestrian crossing speeds, delays, and gap acceptance behavior in Asian nations based on several investigations. Several notable pieces of information were also formulated through tabulation. By averaging the pedestrian speed of some Asian countries, it was found that pedestrians move at 1.23 m/s, and Indonesian pedestrians move slower than other countries’ pedestrians. Gap acceptance behavior depends on age, gender, group/individual, education status, day/night, vehicle type, traffic flow, waiting location etc. Such factors are also the influencing factors for pedestrian speed too. The study’s conclusions will help mitigate traffic safety issues by creating an effective intersection control system. Practitioners and policymakers can use the study results to develop effective management strategies to lessen collisions between pedestrians and vehicles in uncontrolled locations in urban areas.

Quantum Machine Learning (QML) is a field that combines the principles of Quantum Computing (QC) and Machine Learning (ML). QC works by taking advantage of the properties of quantum physics, such as superposition and entanglement. To fully realize the potential of this technology, more research is necessary, as the field of QML is still in its early stages. Since QC technologies and devices continue to develop quickly, it is important to identify the use cases and applications that benefit the most. This paper investigates the potentials of QC, and more specifically, Quantum Annealing (QA), for clustering real-world data in transportation systems. The Bike Sharing System (BSS) is used as a case study applying a clustering model on QA computers. The main contribution of this research is to introduce a hybrid model to cluster stations in a BSS by solving it as a Constraint Satisfaction Problem (CSP) problem with different methods on a QA computer using a real-time dataset. In addition to the practical contribution, this research also offers theoretical advancements in the field of computational optimization by defining a new topology for the input data that is compatible with QC topology (e.g., Chimera topology). The goal of real-time clustering BSS stations based on dynamic and static datasets is, in fact, to assist decision-makers in better managing and minimizing the risk of bike unavailability at each station and rebalancing bikes shared. Three different methods have been used to determine the number of clusters, and Euclidean, Manhattan, Pearson, and Spearman dissimilarity functions have been applied to cluster the stations. The evaluation is done using the magnitude vs. cardinality approach. The distribution of the stations, magnitude, and cardinality of the results indicate the potential to use QC for clustering for a real-world application, e.g., BSS.

Sharp curves in freeways are known to be unsafe design elements since drivers do not expect them. It is difficult for drivers to estimate the radius of a curve. Therefore, drivers are believed to use other cues to decelerate when approaching a curve. Based on previous successful experiences of driven speeds in curves, drivers are thought to have built expectations of safe speeds given certain cues, minimalising risks. This research employs a Bayesian Belief Network to model driver expectations using measured speeds in 153 curves and data on the characteristics of the curve approaches. This model mimics expectations as the probability of measured speeds given certain cues. Using Bayes theorem, prior beliefs on safe speeds are updated towards a posterior belief when a new cue is observed during curve approach. We refer to this posterior belief as expected safe speed. Drivers are assumed to adjust their operating speed if it does not match their expected safe speed. The model shows that the visible deflection angle has a large influence in setting the expectations of a safe speed for an upcoming curve. In addition, the preceding type of roadway and the number of lanes are both important cues to set a driver’s expectations of a safe speed. Speed and warning signs are shown to be interdependent on the road scene and hence have less influence in setting expectations. This research shows that design and safety assessment of freeway curves should be considered aligned with the road scene upstream of the curve.

This paper presents PREDICTOR (PREDICting Take-Over Response time): an interactive open-source research software tool to predict the timing of various stages of a transition of control, or take-over, in semi-automated driving. Although previous work has investigated extensively what factors affect the minimum time needed for a successful take-over by the driver, less is known about how specific stages within the take-over process are affected by those factors. PREDICTOR applies a theoretical framework that describes the take-over process as interruption handling through a series of stages. It then ties this theory to a database that summarizes results from previous take-over studies. PREDICTOR can be used to interactively predict through simulation how specific human factors (e.g., alert modality, alert onset time) impact four distinct stages of the take-over response process. The tool simulates and visualizes expected reaction time distributions for each stage of the take-over process. The use of distributions also highlights the likelihood of an accident – as long responses (“outliers”) are quantifiable. Moreover, it can help understand at which stage drivers might take relatively longer or shorter, and which stages are most impacted by a specific factor (e.g., alert modality). PREDICTOR also allows users to add their own data, and to define their own dependent variables for analysis. As a tool that allows exploration of various scenarios, PREDICTOR can aid in the prediction and analysis of potential future accidents.

The archipelagic state of Indonesia requires an efficiently integrated maritime logistics system. Integrating the maritime logistics system results in advantages for remote and difficult-to-reach areas, especially in the islands, to improve supply chain efficiency for the basic needs of island communities. The study explored how increasing economic activity in islands can enlarge the throughput volume and decrease maritime logistics costs. The method used the dynamic system to investigate the relationship between the sub-systems of regional economics, port throughput, and maritime logistics costs. The result suggested that an economic growth rise of 7,5% influenced the increasing cargo throughput by 2% and decreased sea transportation costs by 0,0059%. Developing logistics centers near islands may lower ship operational costs and effectively solve the inefficiency of maritime logistics costs in the eastern part of Indonesia.

Extremely high levels of PM_2.5 (particulate matter with a diameter of 2.5 micrometers or less) in Bangkok’s major areas can cause a variety of issues, particularly health issues. It is an unavoidable situation for an individual traveling through affected areas by using Bangkok public transportation, which is an affordable travel choice for low- and middle-income Bangkok residents. In this study, a scenario of taking public transportation through affected areas is simulated using graphs and game-theoretic ideas. A game is played on a directed graph constructed from an example of traveling routes between two places in Bangkok. The game consists of players, who are people at a bus stop in Bangkok with the same destination; strategies for each player, which are the possible public transits; and the payoff for a player, which is the maximum amount of PM_2.5 breathed in during the trip. The game’s equilibrium is investigated in terms of how the game responds to the PM_2.5 situation. One of the goals is to use the results in public transportation planning that can make community health and well-being affordable for everyone, which is a part of sustainable development.

Understanding mobility dynamics and their influence on epidemic spread is crucial for effective management strategies, a concept that, despite its importance, has received limited integration in traditional epidemiological models. This study introduces a novel decision support tool that integrates an activity-based model for mobility dynamics with a multi-group compartmental SIRD (Susceptible–Infected–Recovered–Dead) model for infection transmission. The tool consists of a multi-objective optimization framework that evaluates the trade-offs between public health and economic factors in socioeconomic segments. Our findings show that policies targeted at specific demographic groups significantly improve the efficacy of interventions. The framework provides policymakers with a collection of optimized and customized strategies through a user-friendly dashboard, using a multi-objective modeling approach. This visualization compares potential outcomes along the Pareto frontier, helping to select balanced and effective policies. The proposed model offers a significant step forward in epidemic management, providing a robust platform for data-driven decision making in crisis scenarios.

Urban mobility planning is increasingly characterized by the pressure to establish low-emission, space-efficient, and socially inclusive transport services. One such planning task for decision-makers in public institutions is the efficient design of bicycle-sharing networks. This study examines the factors that influence decision-making in this process, both among practitioners and within the research literature on decision support systems (DSS). Employing a dual approach of academic literature review and 16 qualitative interviews with public sector planners, we identify and compare different dimensions of key factors affecting location choice. The results reveal significant contrasts, for instance, between the emphasis placed by academic researchers on ensemble optimization of quantifiable factors and the more target-complex, intuitive approaches pursued by public planning practitioners. We blend core characteristics from both perspectives in a synthesis analysis. We argue that future efforts should aim for more vertical planning assistance, citizen involvement for diversified demand indications, integration of local factors into DSS, and standardized data formats to enable better DSS integration. Our study offers a rare qualitative insight into a complex choice problem faced by public decision-makers, linked with predominantly quantitative research.

Unpredictable shock events have disruptive and long-lasting impacts on the aviation industry. However, the scale and type of impact of different shock events on airport operations and planning have been unevenly surveyed. A better understanding can help airports to improve their risk mitigation and develop more resilient operations and management. Through a systematic literature review of peer-reviewed research, we summarise shock events into four categories based on the cause of the shock: economic recessions, infectious disease pandemics, terrorist attacks, and natural disasters. The major impacts are identified as a reduction in air travel demand, interruptions in operations, modified operating procedures, and changes in facilities and infrastructure. We further bring these together into a conceptual typology of event-impact interactions. This longitudinal overview may assist airport managers in better understanding the impacts of shock events and taking further steps in developing more resilient airport infrastructure and associated business models.