Transportation最新文献

Utilitarian cycling speed is a crucial input for applications such as infrastructure design, mode and route choice models, traffic microsimulation, safety evaluations, and health impact assessments. However, current methods fail to distinguish between average speed and cruising speed, the latter of which is more behaviourally indicative. This study aims to identify cruising speed from GPS data and investigate how it varies with contextual and personal factors. We evaluate six algorithms to extract cruising events from cycling GPS travel data: three time series clustering methods to identify steady-state events, in combination with two labeling methods to identify which events represent cruising. The best-performing algorithm uses Toeplitz Inverse Covariance-Based Clustering and identifies cruising events based on a decision tree heuristic. The average cruising speed of 21.53 km/hr is significantly higher than the overall average speed of 19.95 km/hr. Cruising speeds are higher for commute trips, longer trips, e-cyclists, ‘Dedicated’ cyclists, and men. Regarding route factors, cruising speeds are higher in locations with lower grade, more greenery, on-street cycling facilities, high motor vehicle volume, no traffic controls, and lower relative crash risk. Distinguishing cruising events within cycling trajectory data is necessary to avoid underestimating the behavioural sensitivity of cyclists to factors such as road grade, facility type, relative crash risk, trip purpose, gender, and bicycle motorization.

We innovatively shift the research focus from traditional metro systems to the broader spectrum of urban rail transit systems to study the relationship between rail transit development and urban pollution. Previous studies have predominantly concentrated on metro systems, often overlooking the various forms of rail transit such as light rail, trams, and commuter trains, each with distinct environmental impacts. By broadening the scope to include these diverse modes, our research offers a comprehensive analysis of how urban rail transit systems contribute to pollution reduction in Chinese cities. Utilizing panel data from 2011 to 2023, we investigate the effects of rail transit development on air quality, focusing on two primary mechanisms: replacing taxi usage and lowering per capita traffic energy consumption. Our empirical findings, derived from a Difference-in-Differences approach, reveal that the expansion of urban rail transit significantly reduces urban pollution levels. Additionally, we identify variations in effectiveness across different city sizes and regions, with larger cities and eastern regions experiencing more pronounced benefits. These insights underscore the importance of tailoring urban rail policies to local contexts. The study concludes with policy recommendations aimed at maximizing the environmental benefits of urban rail transit systems.

Conventional methods of rail transit passenger flow forecasting usually use general rail transit data for analysis, such as the spatial structure of the network, the distribution of stations, historical passenger flow, etc. However, these methods tend to focus on forecasting regular passenger flow and are insufficient under special events. With the widespread of social media, special events are often disclosed in advance on social media. The attitudes of citizens towards them become an important factor affecting their travel willingness and mode. Existing models usually ignore people’s sentiment, where people’s sentiment tendencies can influence travel destination choices. Particularly during special events, sentiments expressed on social media can trigger short-term sudden changes in passenger flow, which cannot be effectively achieved using traditional automatic fare collection data alone. Therefore, this paper proposes a deep learning-based forecasting model: passenger flow forecasting model for urban rail transit based on multimodal fusion under special events (PFFM-SE), aimed at improving the accuracy of short-term passenger flow forecasting by incorporating social media sentiment data under special events. PFFM-SE includes a travel sentiment analysis, a point-of-interest association, and an outbound passenger flow forecasting. By integrating long short-term memory networks, variational auto encoders, multi-head cross-attention mechanisms, and convolutional neural networks, this model achieves enhanced forecasting of passenger flows augmented with social media sentiment. The experiments used real-world special events social media sentiment and AFC datasets from two cities in China. The results demonstrate that PFFM-SE outperforms various existing advanced models in passenger flow forecasting under special events.

Simulation is a valuable tool for traffic management experts to assist them in refining and improving transportation systems and anticipating the impact of possible changes in the infrastructure network before their actual implementation. Calibrating simulation models using traffic count data is challenging because of the complexity of the environment, the lack of data, and the uncertainties in traffic dynamics. This paper introduces a novel stochastic simulation-based traffic calibration technique. The novelty of the proposed method is: (i) it performs local traffic calibration, (ii) it allows calibrating simulated traffic in large-scale environments, (iii) it requires only the traffic count data. The local approach enables decentralizing the calibration task to reach near real-time performance, enabling the fostering of digital twins. Using only traffic count data makes the proposed method generic so that it can be applied in different traffic scenarios at various scales (from neighborhood to region). We assess the proposed technique on a model of Brussels, Belgium, using data from real traffic monitoring devices. The proposed method has been implemented using the open-source traffic simulator SUMO. Experimental results show that the traffic model calibrated using the proposed method is on average 16% more accurate than those obtained by the state-of-the-art methods, using the same dataset. We also make available the output traffic model obtained from real data.

Micro-mobility modalities, such as electric scooters and shared bicycles, have gained popularity as urban transport alternatives due to their contribution to environmental care, ease of use, and convenience. There is a growing interest in understanding the adoption and loyalty towards these services, with special attention to gender differences in their use. Therefore, this study aims to identify the factors that determine loyalty in the use of electric bicycles and scooters among women. To achieve this, a survey was conducted among 254 women residing in the Aburra Valley Metropolitan area, in the Antioquia, Colombia department. An integrated model combining the Theory of Planned Behaviour (TPB) and the Technology Acceptance Model (TAM) was established. The analysis used a Partial Least Squares Structural Equation Model (PLS-SEM). The results validated the structure of the model and revealed that only two factors significantly influence loyalty: perceived green value and perceived enjoyment. Perceived usefulness, perceived control, and subjective norm did not prove to be relevant for loyalty. Additionally, a strong relationship was found between consumer innovativeness and women's perceived green attitude, the latter being a key factor positively affecting perceived green value. This study contributes to the existing literature by providing a deeper understanding of the factors influencing women's loyalty to electric bicycle and scooter use from a gender perspective in an emerging economy.

In exploring the societal readiness for shared autonomous vehicles (SAVs) in Swedish cities, this study profiles two distinct user groups—potential users and refusers—based on their willingness to share rides with strangers. By analyzing responses from a significant sample in Stockholm and Gothenburg, the study reveals key traits of these groups: potential users tend to be progressive, environmentally conscious men with public transport habits and positive experiences with AVs, while refusers are often women with traditional values, less formal education, and a preference for private cars, exhibiting concerns about safety and privacy. The research underscores the necessity of addressing the unique concerns of refusers to foster broader acceptance of SAVs. It highlights the potential of SAVs to revolutionize urban transport if societal concerns are aptly managed through policy and education, leveraging positive public transport experiences as a gateway to shared autonomous mobility.

As the populations continue to age across many nations, ensuring accessible and efficient transportation options for older adults has become an increasingly important concern. Autonomous Mobility-on-Demand (AMoD) systems have emerged as a potential solution to address the needs faced by older adults in their daily mobility. However, estimation of older adult mobility needs, and how they vary over space and time, is crucial for effective planning and implementation of such service, and conventional four-step approaches lack the granularity to fully account for these needs. To address this challenge, we propose an agent-based model of older adults mobility demand in Winnipeg, Canada. The model is built for 2022 using primarily open data, and is implemented in the Multi-Agent Transport Simulation toolkit. After calibration to accurately reproduce observed travel behaviours, a new AMoD service is tested in simulation and its potential adoption among Winnipeg older adults is explored. The model can help policy makers to estimate the needs of the elderly populations for door-to-door transportation and can guide the design of AMoD transport systems.

Short-term metro OD demand prediction during public health emergencies is a crucial task for the effective management and operation of metro systems. However, such emergencies tend to cause significant fluctuations in OD demand, making accurate prediction particularly challenging. To tackle this problem, this paper proposes a Multi-Frequency Spatial-Temporal Graph Neural Network (MFST-GNN) to accurately predict the metro OD demand during public health emergencies. Specifically, multiple OD demand patterns, including real-time, daily, and weekly OD demand are leveraged to extract the periodicity spatial-temporal features of OD demand. A novel multi-frequency temporal feature extraction module is developed to capture the periodic temporal features, while an adaptive spatial feature extraction module is introduced to learn the complex hidden spatial features. Moreover, event-related information is collected and integrated into the OD features to study the impact of events on OD demand. The effectiveness of the proposed model is validated by a large-scale real-world metro OD dataset, with comparative analysis against benchmark prediction models. Results demonstrate its superior performance and practical application potential.

Although the transportation sector is a crucial component of economic growth, it also substantially contributes to environmental degradation. Thus, in alignment with the United Nations Sustainable Development Goals (SDGs), the decarbonization of the transportation sector is pivotal for achieving SDG 11. Therefore, this study explores the underlying role of biofuel consumption (BC), green technology innovation (GTI), and environmental policy stringency (EPS) in abating transport sector emissions for eleven emerging economies spanning from 2005 to 2022. This study examines the individual and moderation effects using the cross-sectional augmented autoregressive distributed lag (CS-ARDL) and augmented mean group (AMG) estimators while addressing the heterogeneity and unobserved endogeneity issues. The empirical findings posit that while biofuel has a positive impact, EPS has a modest but negative impact on transportation emissions. Conversely, GTI significantly abates transportation emissions in emerging countries. Besides, the moderation effect of EPS with BC and GTI significantly abates transportation emissions in the long run. Thus, this study suggests implementing coherent policies that include tax benefits, biofuel mandates to produce advanced biofuel products, and coordinated efforts to strengthen institutional and regulatory frameworks to decarbonize the transportation sector and achieve sustainable goals.

Tackling India’s contribution to global carbon emissions is a priority from both national and international perspectives. Energy efficiency gains in Indian’s transportation sector have been suggested as a promising way to mitigate carbon emissions. The Indian government is considering fuel efficiency labels for new passenger cars. Via a discrete choice experiment, this paper investigates how regulatory and non-regulatory interventions can be used to boost adoption of energy efficient cars in India. It estimates New Delhi’s car buyers’ willingness to pay (WTP) for a car displaying a best-efficiency label (which is about 54 to 85% more fuel efficient as compared to a usual car) to be 6 thousand USD or about 30% of what respondents would be willing to pay for a new car. However, the informational nudge embedded in labeling systems may not be enough to boost uptake of efficient cars. Thus, via a split-sample approach, it further investigates the potential of combining non-regulatory interventions—labeling system and peer effects—with a driving restrictions regulation. WTP for a best-efficiency label car increases by over 100% to 13.46 thousand USD under a driving restrictions regulation. The difference in WTP for a best-efficiency label across driving restrictions and no driving restrictions scenarios reflect regulatory costs faced by car drivers. By including an interaction effect between best-efficiency label and mileage in the econometric specifications, we show that these costs depend on the actual mileage of the car under consideration—with lower regulatory costs as actual efficiency improves. A latent class logit specification suggests that around 40% to 52% of respondents—labeled extrinsically-motivated adopters—would be responsive to peer effects.