Transportation最新文献

Anti-congestion policies, such as urban spatial planning, transport infrastructure, and economic incentives, are often regarded as effective structural measures for relieving citywide traffic congestion. However, few studies have empirically investigated and compared the intervention effects of such structural policies on relieving congestion. Using longitudinal AutoNavi’s big-data-based congestion delay index from 96 congested Chinese cities, we developed panel regression models with random effects to estimate the impact of four types of structural determinants on traffic congestion, including fuel price, road construction, public transportation, and urban spatial characteristics. The empirical results demonstrate that (1) the impacts of urban spatial characteristics and public transportation outweigh the impacts of fuel price and road construction on traffic congestion in terms of significance level and quantity; and (2) higher gasoline prices, road length and capacity expansion, a polycentric urban structure, and mixed land use contribute to alleviating traffic congestion. These findings enable a systematic understanding of the determinants of traffic congestion and provide policy implications in the context of developing countries.

In this article, we formulate a hybrid model that allows to identify the triggers of instant utilities using psychophysiological indicators (PPIs). Instant utilities are understood as momentary emotions perceived in every instant of an experience. We build the model using transport and environmental variables associated with the experience to explain instant utilities, which are measured by PPIs and stated emotions. The model is estimated with data from a real-life travel experiment, in which SKT (skin temperature), HR (heart rate), HRV (heart rate variation), and EDA (electrodermal activity) were measured with a wristband. In addition, environmental variables such as CO₂, noise, brightness, and temperature were collected and used to explain instant utility and to control for variation of PPIs. As emotions can be discomposed into at least two dimensions (valence and activation) we capture this multidimensionality estimating two independent models that explain the valence and activation of stated emotions. To analyse what is gained by including physiological data, these models are compared with baseline models without PPIs. Our main findings are: (1) instant utilities are sensible, for instance, to the travel mode; velocity; crowding; brightness; temperature; and humidity; (2) PPIs help to identify the effect of stimuli that cause small variations in the underlying emotions; and (3) instant utility has heterogeneous effects on PPIs across individuals, implying that it is necessary individuals-specific considerations to infer instant utility from PPIs. We discuss the potential applications of this framework in the evaluation of travel satisfaction and demand estimation.

The rapid growth of passengers has led to overcrowding in urban rail transit (URT) systems, especially during holidays, posing significant challenges to the safe management and operation of URT systems. Accurate and real-time short-term passenger inflow and outflow prediction on holidays is essential for operation management and resource allocation to alleviate such overcrowding. However, short-term passenger inflow and outflow prediction on holidays is a challenging task influenced by various factors, including temporal dependencies, spatial dependencies, the temporal evolution of spatial dependencies, the interaction between inflow and outflow, and the limited holiday samples. To address these challenges, we propose a Spatial–Temporal Multi-Task Learning (STMTL) for short-term passenger inflow and outflow prediction on holidays in URT systems. STMTL comprises three parts: (1) Multi-Graph Channel Attention Network (MGCA) extracts both static and dynamic spatial dependencies from inter-station interaction graphs and then adaptively integrates multi-graph features. (2) Time Encoding-Gated Recurrent Unit (TE-GRU), utilizes time encoding gates to capture long-term periodic variations and unique fluctuations caused by holidays. (3) Cross-attention block (CAB) captures complex interactions during holidays and facilitates the sharing of spatiotemporal features between passenger inflow and outflow. The effectiveness and robustness of STMTL are validated on two real-world datasets from the Nanning URT system in China during the New Year’s Day period. Experimental results demonstrate that STMTL consistently outperforms several classic and state-of-the-art models. STMTL achieves a 3.87% and 3.39% average improvement over the best-performing baseline models at 15-min and 30-min granularities, highlighting its potential for practical applications in URT systems during holidays.

The promotion of electric vehicles (EVs) poses challenges to the power grid due to the large-scale and disordered charging behaviors. While previous studies have investigated the charging patterns of EVs, little attention has been paid to electric taxis (ETs). To address this gap, this study proposes a novel combinatorial clustering model to investigate the charging patterns of ETs. This model employs Principle Component Analysis (PCA) for dimensionality reduction, an Canopy + to determine the optimal number of clusters, and concludes with K-means for rapid clustering. It exploits the rich information from the high-dimensional features, such as battery status, time, driving range, and environmental conditions, and enables fast and accurate analysis of large-scale ET charging behavior. The model analyzed a year of charging data from 164 ETs in Hangzhou, identifying six typical patterns. The impact of 20,000 ET charging loads on the power grid was further simulated. The results indicate that increasing the proportion of three types of fast-charging patterns can alleviate the peak and standard deviation of the power load of the grid. This study contributes to a better understanding of the charging behaviors of ETs and provides insights for managing the power demand in the context of urban transportation.

Examining commuting and its connection to wellbeing is a crucial policy concern. Commuting in urban areas is highly stressful and unsafe due to the dearth of transportation supply. Studies examining the cascading phenomenon of commute stress and safety possibly affecting travel wellbeing (TWB) and subsequently impacting quality of life (QOL) are limited. Hence, this study examined the role of trip characteristics, stress and safety perceptions, and residence area characteristics on TWB and how TWB and all these factors further affect the QOL of commuters in a heterogeneous urban region using confirmatory factor analysis and structural equation modeling (SEM). This study further analyzed the equitable distribution of TWB and QOL perceptions across socio-economic groups with the Gini index. A revealed preference survey was conducted in the Mumbai Metropolitan Region, India, and data was collected from 1431 commuters from diverse socio-economic groups. The results indicate that travel time, travel cost, travel discomfort, waiting time, and perceived stress negatively influence TWB, while perceived safety and travel mode are positively associated with TWB. Surprisingly, non-motorized commuters have the lowest TWB levels. Considering direct effects, TWB positively influences QOL, while travel discomfort negatively influences QOL. In indirect effects, perceived stress negatively influences QOL through TWB, whereas perceived safety positively influences QOL through its impact on TWB. The calculated Gini indexes imply equitable distribution of TWB and QOL perceptions among socio-economic groups segmented by income, age, and gender. The policy implications for improving TWB and QOL are discussed accordingly.

Decarbonizing aviation is challenging as few scalable technological alternatives exist, and travel activity is increasing rapidly. It is thus essential to better understand the drivers of air travel behaviour. Previous cross-sectional research has identified a range of factors associated with individual air travel frequency. There is, however, a lack of longitudinal studies identifying the factors associated with change in air travel frequency on the individual level. This is in contrast with research on daily travel and car use, where ‘mobility biographies’ studies have identified the life-course factors associated with travel behaviour change. Our study contributes to filling this gap. We investigate the determinants of change in air travel frequency using data from two waves of the UK Household Longitudinal Survey (2012–2013 and 2018–2019), combined with geographical information at the neighbourhood level. With regression models, we assess the impact of changes in a wide range of factors including socio-demographic and economic situation; residential location; spatial dispersion of social networks; migration status; car ownership; and environmental attitudes. We find significant effects for several variables, including e.g., a negative effect of having children on air travel frequency, and a reduction in the number of flights in the first few years after migrating to the UK. We conclude by discussing how the findings can inform debates on: i) the impact of life-course events on travel behaviour; ii) the causal drivers of air travel frequency; iii) the drivers of air travel growth, and related implications in terms of inequality and ‘institutionalisation’ of air travel.

This study uses the Dynamic Spatial Difference-in-Differences model (Dynamic SDID) to analyze the impact of the Taiwan High-Speed Rail (THSR) on Taiwan’s tourism demand. To control for spillover effects, the model incorporates the Taiwan Tourist Shuttle service (TSHU) as an alternative transportation option, the interactive effects between TSHU and THSR, and the spatial autocorrelation between TSHU and THSR. The analysis results indicate that controlling for spillover effects is crucial for analyzing the impact of the High-Speed Rail and tourist transit service on Tourism Demand, and the Dynamic SDID is a better analytical model for this purpose. The THSR has a significant positive impact on tourism demand, while its spatial autocorrelation effect is significantly negative. This suggests that the increase in tourist traffic brought about by THSR mainly comes from existing tourists in the surrounding areas rather than generating new tourism demand. The TSHU, on the other hand, has a negative but insignificant impact on tourism demand, but its interaction with THSR has a significant positive effect, indicating that the two services complement each other. Therefore, to enhance Taiwan’s tourism demand, the focus should still be on improving the attractiveness of tourist destinations rather than solely relying on the construction of the High-Speed Rail. Additionally, while the TSHU does not contribute significantly to the development of specific individual tourist destinations, it does facilitate regional tourism development. Therefore, selecting TSHU routes based on actual market conditions can promote the growth of the tourism industry.

This paper investigates the impact of conformity on traveler’s route choice and evaluates the value of conformity in this context. Drawing from classic theoretical model of conformity, we analyze the factors influencing conformity in route choice. Based on this analysis, we develop an integrated choice and latent variable (ICLV) model, incorporating latent variables related to conformity, individual characteristics, as well as route-specific attributes. The model parameters are calibrated using data collected from a stated preference (SP) survey in Nanjing, China. Estimation results reveal strong correlations between conformity and travelers’ route choices. The findings of this study carry significant insights for leveraging conformity in the design of navigation software and congestion pricing strategies.

Utilising the existing infrastructure in railway transit to tackle overcrowding requires more understanding of how people use spaces at stations. This study investigated passenger behaviour while waiting for a train on the platform using the data of the Wi-Fi location tracking systems. The trajectories of 129,354 devices were observed in two weeks at two MRT Circle Line stations in Singapore, which have the escalator/stair landings in different positions. A data cleaning process was proposed to overcome the drawbacks of Wi-Fi-based position data. A decomposition method was further developed to separate the walking and staying phases based on data processing. The boarding passengers’ on-platform behaviour was analysed from four aspects: the number of staying phases, the location distributions of different kinds of stays, the location distribution of in-between stays by hour and duration, and the distance and walking speed of the first walking phase. Our results suggested that many passengers (44% and 37% of passengers at the two case study stations) had multiple staying phases, meaning that they did not go directly to their final boarding points after coming to the platform but rather made stops or walkarounds before coming to boarding points. The distributions of locations of the last and in-between stays were significantly different and may influenced by the width, length and layout (such as landing locations) of stations. In addition, the walking speeds of passengers observed on the metro platform were slower than those observed on the streets. These findings indicated that some commonly used assumptions in most simulation models are not true according to the empirical observation. The obtained knowledge would deepen the understanding of the passengers’ on-platform behaviour and thus provide implications for designing railway stations and planning station operations.

This paper presents a novel adaptive stated choice experiment to capture range anxiety during long-distance travel. It is assumed that respondents have forward-looking properties allowing them to select from a set of charging alternatives along the route or postpone charging for a later (choice) stage. Data was collected among members of the Danish electric car association. Based on this data, we develop a mixed logit model that reveals several interesting findings. First, we quantify a relationship between the probability to charge and the remaining range. Secondly, we find that range anxiety, and thereby battery utilisation between recharges is indeed a heterogeneous quantity that varies among user groups. Tesla drivers and individuals below 50 years of age are less prone to range anxiety compared to other segments. Finally, the results suggest that charging at the early stages of a trip is indeed likely even when the battery level is high.