Transportation最新文献

Insufficient parking has emerged as a critical global transportation concern, particularly in central business districts, leading to congestion in urban transport networks with significant social, economic, and environmental implications. In Debre Markos city, the demand for taxis and travel is swiftly rising due to population growth. However, existing road facilities are inadequate, reducing road capacity, mobility, and exacerbating traffic congestion. On-street parking, encompassing parking occupancy, double parking, differences in peak hours between restaurant activity and passing traffic, lack of driver discipline, and the reduced maneuverability of older vehicles, all contribute to narrowing the available road width, thus impeding traffic flow. This study aims to bridge this knowledge gap by investigating the traffic congestion caused by on-street parking and its impacts on traffic flow. Study results indicate that on-street parking significantly reduces road capacity by 24.1% due to legal parking activities and an additional 25.89% due to illegal double parking. Removing on-street parking could enhance road capacity by 49.9% and reduce travel time by 36%. Additionally, the model reveals a strong relationship between on-street parking and the delay of vehicle movement toward parking locations, with each increase in parking occupancy decreasing average travel speed by 0.03 km/h. This study emphasizes the necessity for proactive policies to address parking issues and uphold urban street service levels amid increasing traffic demands. Local authorities can use the model as a guide for implementing parking prohibition policies, including utilizing dead-end roads for short-term parking, enhance enforcement of parking regulations, integrate parking management with urban planning, and implementing parking management measures to alleviate congestion.

The urban majority in Africa do a great deal of walking, yet we do not fully understand the lived realities of the so-called captive walkers, who have no option but to walk. This study explores the everyday lived accounts of urban residents as they navigate the walking environment in two low-income neighbourhoods in Accra, Ghana’s capital. The study adopted a qualitative approach drawing on 2 focus group discussions, 60 interviews with residents in the Dome and Accra Newtown neighbourhoods in Accra, and 10 institutional interviews. The findings show that residents viewed walking as a means of enhancing social relations, health, and spatial awareness. Lived accounts show that walking is stressful and dangerous because of the design and behavioral barriers in the walking environment. While highlighting the value of community-level responses to walking barriers, this paper calls for a more nuanced understanding of the everyday lived experiences of walking, reconsidering walkability challenges as intricately linked to, not separate from, urban development challenges and engaging captive walker perspectives as the basis for driving equitable and inclusive principles in the agenda for sustainable urban mobilities in Africa and Global South generally.

To mitigate climate change and safeguard energy security, it is necessary to limit car dependence, reduce car weights, and shift to alternative car powertrains. This study therefore looked into the real-world specific energy consumption and (hbox {CO}_{{2}}) emissions of cars in the Netherlands. Next, it analyzed how sociodemographic and built environment variables influence energy-relevant car type choices with a multilevel discrete choice modeling framework. At a first stage, this framework considered the number of cars owned. Conditional on that decision, it simultaneously considered choices between different car fuel types and weight categories. The results showed that small, lower-income households with few male or older members in non-green (urban) environments were more likely to own light (efficient) vehicles. Remote households had a preference for light and diesel vehicles. In contrast, households with private parking tended to own heavy and electric vehicles. Finally, owning multiple cars was correlated with both non-urban living and heavy car preferences. The combined effect was a mild preference for energy efficient vehicles in urban areas. Previous studies that omitted vehicle energy efficiency thus slightly underestimated urban planning’s environmental impact. However, our results indicate that the built environment has a greater effect on travel energy use through the number of cars owned than through car specific energy consumption. The bias in the official vehicle energy data was also much larger than the total influence of the built environment on car specific energy consumption.

The radiation model is widely used in traffic demand forecasting. However, when applying the radiation model in the aviation field, it overestimates short-distance travel demand, does not consider flight capacity constraints, and cannot respond well to large-scale disruptions such as COVID-19. Based on the principle of the radiation model, this paper modifies the radiation model using the adjacency matrix of the airline network, along with the total passenger inflow and outflow for each city, flight capacity, and effective distance. The modified radiation model’s performance is verified using the 2018 and 2020 Chinese air passenger flow datasets under normal conditions and large-scale disruptions. The study compared the performance of the modified radiation model with the original radiation model and the opportunity priority selection model on an airline network that includes major cities. The results indicate that the modified radiation model is superior to the original radiation model and opportunity priority selection model in modeling airline networks. Additionally, the passenger travel distances provided by the modified radiation model are more accurate. The modified radiation model can better estimate air traffic demand in airline networks and withstand significant disruptions to airline network traffic.

Household travel surveys collect core datasets for modelling passenger travel demand. However, the decline in survey completion rate has become a concern in recent years. Among all components, the travel diaries are the most challenging part of CAWI travel surveys and suffer significant dropouts of participation. Therefore, an investigation is necessary to understand the influential factors contributing to participation dropouts while reporting their travel dairies and subsequent non-response bias on data quality. This study utilizes binary logit models to capture the relationship between respondents’ drop-off behaviours while filling out travel diaries, their socioeconomic characteristics and different travel diary designs. The study also develops a generalizable analysis framework to measure the impact of non-response bias on data quality. The analysis framework incorporates a trip generation model with microsimulation and bootstrapping techniques. The results show that a diary design with stable repetitions is preferred by respondents and results in less non-response bias in the final dataset. This study also proposes recommendations for future travel surveys.

Accessibility improvement has been regarded as one of the main regional policy goals in the recent decades. As a result, many countries has undertaken complex transport infrastructure investment programmes worth billions of dollars. The best examples of such investment can be China or, in the European context, Spain and more recently Poland. There have been published many analyses focusing on output and employment effects of transport infrastructure investment projects. Yet, there hardly exist any studies that verify their potential influence on interregional trade flows. This paper fills the existing gap in the literature by assessing the impact of accessibility improvement on the volume and the value of interregional trade flows in Poland between 2005 and 2015. We find that there is no statistically significant relationship between travel time reduction and interregional trade. This may explain largely the surprising lack of positive relationship between accessibility improvement and regional GDP growth, reported in previous papers.

The transition to autonomous vehicles (AVs) will likely vary across countries due to differences in technology advancements, infrastructure, cultural background, and policy. Managing this transition can be challenging, as similar policies may elicit different responses from relevant stakeholders, leading to either societal benefits from the technology or unintended consequences. This study explores the similarities and differences in the impacts of sociodemographic factors, attitudinal factors, and policy-related factors on AV adoption in two countries that are expected to be one of the early adopters: China and the United States. A theoretical framework was developed to examine these impacts, and Multiple Indicators and Multiple Causes models were estimated using 2000 valid survey responses from each country. The model estimation results reveal that certain sociodemographic factors, such as education and income levels, similarly influence AV adoption intentions in both countries. However, other characteristics, like gender and the number of household vehicles, exhibit contrasting effects. Furthermore, attitudinal factors like attitude towards AVs, perceived usefulness, and perceived monetary value significantly impact AV adoption intentions among Chinese respondents, whereas perceived usefulness, perceived nonmonetary value, and subjective norms play a more prominent role in shaping AV adoption intentions among American respondents. In terms of policy impacts, individuals already inclined towards using AVs are more likely to strengthen their intention under all policies, with a more pronounced effect in China, where cultural and economic factors, along with stronger government support for technology, play a significant role.

Quality of life (QoL) in daily travel is increasing in popularity as a research topic since transportation infrastructures and services are instrumental in accessing basic services and social capital benefits in areas such as public health, employment, housing, etc. This accessibility has consequently led to improved QoL for the Bangkok population. In this study, the evaluation of the perception of QoL during the daily travel of Bangkokians in Sukhumvit District, Thailand is conducted using face-to-face interview questionnaires with 500 respondents. The structural equation model (SEM) is employed to quantify QoL and its related multidimensional determinants. Four statistically significant factors affect QoL from the travel perspective: (1) accessibility (p-value 0.001), (2) travel cost (p-value 0.05), (3) environment (p-value 0.05), and (4) information (p-value 0.05). Interestingly, accessibility was found to have the most influence on QoL in daily travel. Therefore, policymakers are recommended to consider the degree to which QoL may be affected to establish transportation policies that are more acceptable, practical, and efficient.

To effectively manage and control public transport operations, understanding the various factors that impact bus arrival delays is crucial. However, limited research has focused on a comprehensive analysis of bus delay factors, often relying on single-step delay prediction models that are unable to account for the heterogeneous impacts of spatiotemporal factors along the bus route. To analyze the heterogeneous impact of bus arrival delay factors, the paper proposes a set of regression equations conditional on the bus location. A seemingly unrelated regression equation (SURE) model is developed to estimate the regression coefficients, accounting for potential correlations between regression residuals caused by shared unobserved factors among equations. The model is validated using bus operations data from Stockholm, Sweden. The results highlight the importance of developing stop-specific bus arrival delay models to understand the heterogeneous impact of explanatory variables. The significant factors impacting bus arrival delays are primarily associated with bus operations, such as delays at consecutive upstream stops, dwell time, scheduled travel time, recurrent congestion, and current traffic conditions. Factors like the calendar and weather have significant but marginal impacts on arrival delays. The study suggests that different bus operating management strategies, such as schedule adjustments, route optimization, and real-time monitoring and control, should be tailored to the characteristics of stop sections since the impacts of these factors vary depending on the stop location.

The growing behaviors of work-from-home (WFH) and online shopping hold significant potential for reducing traffic congestion and emissions. Understanding the frequency and the interplay between these two behaviors is important for successful implementation. This study investigates the recent trends of WFH and online shopping and the underlying factors influencing individuals’ decisions on these two behaviors. Focusing on non-grocery online shopping, this study uses comprehensive survey data collected across the United States during October and November 2021. We develop a Generalized Structural Equation Model to jointly examine WFH and online shopping frequency and their interaction. Moreover, the study investigates the psychological aspects of WFH and online shopping, introducing four stochastic latent constructs—WFH comfort, WFH unproductiveness, online shopping enjoyment, and online shopping inconvenience using the attitudinal variables. Results indicate a positive causal relationship, suggesting that increased WFH promotes online shopping engagement. Perceived comfort and productivity at home affect WFH frequency shaped by factors like home workspace, commuting time, childcare responsibilities, and telecommunications with co-workers. Likewise, perceived convenience and enjoyment significantly affect online shopping, influenced by aspects such as timesaving, and the delivery and return process. Technological tools at home also play a role in WFH frequency. Demographic factors like age, race, income, physical disability, and mode choice habits correlate with WFH and online shopping incidence, while job category and employer flexibility influence WFH frequency. These insights can help policymakers to regulate remote work and online shopping activities as they continue to grow.