Search Results (18)

This study aims to improve the predictive accuracy of metropolitan planning organizations’ (MPOs’) travel demand models (TDM) by unraveling the factors influencing transportation mode choices. By exploring the interplay between trip characteristics, socioeconomics, built environment features, and regional conditions, we aim to address existing gaps in MPOs’ TDMs which revolve around the need to also integrate non-motorized modes and a more comprehensive array of features. Additionally, our objective is to develop a more robust predictive model compared to the current nested logit (NL) and multinomial logit (MNL) models commonly employed by MPOs. We apply a one-vs-rest random forest (RF) model to predict mode choices (Home-based-Work, Home-Based-Other, and non-home-based) for over 800,000 trips by 80,000 households across 29 US regions. Validation results demonstrate the RF model’s superior performance compared to conventional NL/MNL models. Key findings highlight that increased travel time and distance are associated with more auto trips, while household vehicle ownership significantly affects car and transit choices. Built environment features, such as activity density, transit density, and intersection density, also play crucial roles in mode preferences. This study offers a more robust predictive framework that can be directly applied in MPO TDMs, contributing to more accurate and inclusive transportation planning. Full article

The motorization of school commutes reduces the physical activity of children and causes a series of urban traffic and social problems, such as traffic congestion in school districts and parents becoming necessary for transportation. To alleviate traffic jams and related social problems, as well as to encourage physical activity amongst students, we advocate non-motorized travel modes for students, such as walking and cycling. Based on a case study of the Wuhan East Lake High-Tech Development Zone, we use a multiple linear regression model to analyze the relationship between influence factors and student travel mode choices. The results show that built environment factors (the built environment factors are divided into density, diversity, accessibility, and destination) have a significant impact on school travel mode choices, especially accessibility and diversity. Furthermore, the study highlights the pivotal role of travel perceptions, particularly perceptions of safety, comfort, and convenience. Through a questionnaire survey, we collect students’ travel perceptions and their actual school travel modes, which offer valuable insights for urban planners and policymakers. The findings indicate the complex interplay between student commuting and the built environment. Additionally, these findings can be valuable, both in academia and for policymakers. We provide strategies that could be beneficial for reducing motor vehicle activities (especially driving). Full article

Travel mode choice (TMC) prediction, improving health parameters, and promoting sustainable transportation systems are crucial for urban planners and policymakers. Past studies show the influence of health on activities, while several studies use multitasking activities and physical activity intensity to study the association between time use and activity travel participation (TU and ATP) and health outcomes. Limited studies have been conducted on the use of transport modes as intermediate variables to study the influence of TU and ATP on health parameters. Therefore, the current study aims to evaluate urban dependency on different transport modes used for daily activities and its influence on health parameters to promote a greener and healthier society and a sustainable transportation system. Pearson’s Chi-squared test was used for transport mode classification, and multinominal logit models were used for regression using R programming. A total of five models were developed for motorized, non-motorized, public transport, physical, and social health to study the correlation between transport modes and health parameters. The statistical analysis results show that socio-demographic and economic variables have a strong association with TMC in which younger, male, workers and high-income households are more dependent on motorized transport. It was found that a unit rise in high-income causes a 4.5% positive increase in motorized transport, whereas it negatively influences non-motorized and public transport by 4.2% and 2.2%, respectively. These insights might be useful for formulating realistic plans to encourage individuals to use active transport that will promote sustainable transportation systems and a healthier society. Full article

E-scooters have emerged as a popular micromobility option for short trips, with many cities embracing shared e-scooters to enhance convenience for travelers and reduce reliance on automobiles. Despite their rising popularity, there is a lack of clear understanding of how user preferences and adoption practices vary by location. This study aims to explore user and non-user attitudes towards e-scooter use in diverse urban settings. A meta-analysis of data from three surveys (N = 1197) conducted in Washington, D.C., Miami, FL, and Los Angeles, CA, was performed to compare e-scooter users and non-user profiles, mode choice factors, and attitudes and preferences towards e-scooter use. Additionally, machine learning (ML) and SHAP (SHapley Additive exPlanations) analysis were utilized to identify influential factors in predicting e-scooter use in each city. The results reveal that the majority of e-scooter users are 25 to 39 of age, male, with higher income and a bachelor’s degree, and 92% possess a driver’s license. Significant differences in attitudes between e-scooter users and non-users highlight the complexity of perceptions towards e-scooter usage. The ML model indicates that employment status negatively impacts the prediction of e-scooter users, while factors such as living without a car and using non-motorized modes positively influence e-scooter use. Educational background is a significant e-scooter mode choice factor in Washington, D.C. and Miami, whereas attitudinal questions on car and technology usage are influential in Los Angeles. These findings provide valuable insights into the factors shaping e-scooter adoption, informing urban transportation planning and policymaking and enhancing understanding of shared micromobility and its impact on urban mobility. Full article

An influx of new mobility trends such as fare-free bus transportation, ride hail, and e-scooter services to improve access and affordability of transportation on campus may be shifting the travel behavior of campus patrons such that it affects their long-term health outcomes. The main research questions explored in this study are as follows: (1) why university patrons choose new modes of travel?; (2) what existing mode did the new modes of travel replace for the riders?; and (3) is the average body mass index (BMI) of users primarily using non-motorized transit options lower than those using motorized or both (referred to as hybrid) for on-campus travel needs? An online survey was administered to a campus community (n = 3309) including students (48%), faculty (15%), and staff (37%) in fall of 2018 when fare-free bus transportation and e-scooters became available on campus, and a gradual increase in ridership of ride-hail services was simultaneously observed. This study found that campus patrons were more inclined to replace active modes of travel with affordable and accessible modes of transportation, thereby substituting their walking or biking routine with app-based transportation services. The mean BMI among travelers who chose motorized transportation modes was more than active travelers, and the BMI was statistically significantly associated with age, gender, race, class standing (undergraduate/graduate), and residence on/off campus. This study concludes with suggestions to prevent substitution of active with non-active transport choices and provides policy guidelines to increase awareness on achieving physical activity levels through active modes of travel for university patrons. Full article

The analysis and modeling of parameters influencing parents’ decisions regarding school travel mode choice have perennially been a subject of interest. Concurrently, the evolution of artificial intelligence (AI) can effectively contribute to generating reliable predictions across various topics. This paper begins with a comprehensive literature review on classical models for predicting school travel mode choice, as well as the diverse applications of AI methods, with a particular focus on transportation. Building upon a published questionnaire survey in the city of Thessaloniki (Greece) and the conducted analysis and exploration of factors shaping the parental framework for school travel mode choice, this study takes a step further: the authors evaluate and propose a machine learning (ML) classification model, utilizing the pre-recorded parental perceptions, beliefs, and attitudes as inputs to predict the choice between motorized or non-motorized school travel. The impact of potential changes in the input values of the ML classification model is also assessed. Therefore, the enhancement of the sense of safety and security in the school route, the adoption of a more active lifestyle by parents, the widening of acceptance of public transportation, etc., are simulated and the impact on the parental choice ratio between non-motorized and motorized school commuting is quantified. Full article

There have been numerous studies on the impact of COVID-19 on mobility in most developed countries; however, few of the studies have focused on the impact of the pandemic in developing countries, especially in Africa. In view of this, our study examined the impact of the pandemic on residents’ transportation mode choice in South Africa. This study adopted the use of both primary and secondary data obtained from TomTom statistics and an online survey of respondents’ mobility patterns before and during the pandemic. The questionnaire was administered through emails, and respondents were asked to provide information about their socio-economic characteristics, travel characteristics (before and during COVID-19), and the effect of COVID-19 on their travel patterns. A multinomial logistic model was adopted for analysis, and the findings revealed that variations existed in trip frequency, trip purpose, and mode choice of people before and during the pandemic. It was also discovered that respondents shifted from the use of public transport to private cars during the pandemic as a result of the implications for their health. Based on this, we propose that an enabling environment and an efficient transport planning technique should be adopted by the government and relevant stakeholders in the transport sector. This will integrate all modes of transport to reduce the over-reliance on private automobiles and also to encourage the use of non-motorized transport (walk/cycle) for sustainable transport planning in the future. Full article

In this study, we aimed to evaluate the performance of various machine learning (ML) classifiers to predict mode choice of movement-challenged persons (MCPs) based on data collected through a questionnaire survey of 384 respondents in Dhaka, Bangladesh. The mode choice set consisted of CNG-driven auto-rickshaw, bus, walking, motorized rickshaw, and non-motorized rickshaw, which was found as the most prominent mode used by MCPs. Age, sex, income, travel time, and supporting instrument (as an indicator of the level of disability) utilized by MCPs were explored as predictive variables. Results from the different split ratios with 10-fold cross-validation were compared to evaluate model outcomes. A split ratio of 60% demonstrates the optimum accuracy. It was found that Multi-nominal Logistic Regression (MNL), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) show higher accuracy for the split ratio of 60%. Overfitting of bus and walking as a travel mode was found as a source of classification error. Travel time was identified as the most important factor influencing the selection of walking, CNG, and rickshaw for MNL, KNN, and LDA. LDA and KNN depict the supporting instrument as a more important factor in mode choice than MNL. The selection of rickshaw as a mode follows a relatively normal probability distribution, while probability distribution is negatively skewed for the other three modes. Full article

The energy consumption and emissions in the urban transportation are influenced not only by technical efficiency in the mobility operations but also by the citizens’ mobility behaviors including mode choices and modal shift among sustainable and unsustainable mobility modes. Information and Communication Technologies (ICTs) can play an important role in the mobility behaviors of citizens, and it is necessary to study whether ICTs support sustainable mode choices like public transport and nonmotorized modes, which increase the total energy efficiency in the urban mobility and reduce traffic congestion and related emissions. This paper focuses on the two most popular ICT services in the urban transport, which are ATIS (Advanced Traveler Information Systems), and ridesourcing services. This study used the New York Citywide Mobility Survey (CMS) findings with a sample of 3346 participants. The associations between using these two ICT services and the mobility behaviors (mode choice with ATIS and modal shift to ridesourcing) are analyzed through a multinomial logistic regression and descriptive statistics, and the results are compared with similar international studies. The findings indicate that the respondents who use ATIS apps more frequently are more likely to use rail modes, bicycles, bus/shuttles, and rental/car sharing than private cars for their work trips. Moreover, the findings of the modal shift to ridesourcing indicate that the most replaced mobility modes by ridesourcing services are public transport (including rail modes and buses), taxis, and private cars, respectively. Full article

Millions of individuals rely on urban transportation every day to travel inside cities. However, it is not clear how route parameters (e.g., traffic conditions, waiting times) influence users when selecting a particular route option for their trips. These parameters play an important role in route recommendation systems, and most of the currently available applications omit them. This work introduces a new hybrid-multimodal routing algorithm that evaluates different routes that combine different transportation modes. Hybrid-multimodal routes are route options that might consist of more than one transportation mode. The motivation to use different transportation modes is to avoid unpleasant trip segments (e.g., traffic jams, long walks) by switching to another mode. We show that the possibility of planning a trip with different transportation modes can lead to improvement of cost, duration, and quality of experience urban trips. We outline the main research contributions of this work, as (i) an user experience model that considers time, price, active transportation (i.e., non-motorized transport) acceptability, and traffic conditions to evaluate the hybrid routes; and, (ii) a flow clustering technique to identify relevant mobility flows in low-sampled datasets for reducing the data volume and allow the execution of the analytical evaluation. (i) uses a Discrete Choice Analyses framework to model different variables and estimate a value for user experience in the trip. (ii) is a methodology to aggregate mobility flows by using Spatio-temporal Clustering and identify the most relevant of these flows using Curvature Analysis. We evaluate the proposed hybrid-multimodal routing algorithm with data from the Green and Yellow Taxis of New York, Citi Bike NYC data, and other publicly available datasets; and, different APIs, such as Uber and Google Directions. The results reveal that selecting hybrid routes can benefit passengers by saving time or reducing costs, and sometimes both, when compared to routes using a single transportation mode. Full article

The objective of this study was to identify the effects of land-use characteristics on the transport mode choices of people according to their purpose of travel. Land-use characteristics consisting of variables associated with density, diversity and accessibility were selected as independent variables. The volume of traffic entering each administrative neighborhood was extracted to establish travel data as the dependent variable. We compared and analyzed the results derived from ordinary least squares (OLS) analysis and spatial regression (SR) analysis. The results showed that the explanatory power of the SR model was higher than that of the OLS model. The results in this study reveal that the effects of land-use characteristics on travel show clear differences according to the transport mode, more so than according to the purpose of travel. Moreover, the results showed that an increase in the level of variables associated with density does not always facilitate the use of non-motorized or public transit modes, nor does it always deter the use of personal motorized modes. The findings in this study are significant in a knowledge-sharing context, as they present the effects of land-use characteristics on the volume of traffic in high-density cities, using Seoul as a case study. Full article

Regarding the sharp growth rate of ICT (information and communication technology)—based mobility services like ridesourcing, it is essential to investigate the impact of these new mobility services on the transport mode choices, particularly on active mobility modes like cycling. This impact is more important in the MENA context (the Middle East and North Africa), where cycling does not constitute the main mobility mode in the modal split of most MENA cities. This paper studies the relationship between the regular use of ICT-based mobility services like ridesourcing and the tendency to cycle to near destinations. This paper contains the analysis of 4431 interviews in two large cities of the MENA region (Cairo and Tehran). This research uses logistic regression to analyze and compare the odds of cycling among regular and non-regular users of ridesourcing by considering the socio-economic, land use, and perception variables. The findings indicate that the odds of cycling among the regular users of ridesourcing are 2.30 and 1.94 times greater than these odds among non-regular ridesourcing users in Tehran and Cairo, respectively. Therefore, the regular users of ridesourcing are more likely to cycle to their near destinations than non-regular ridesourcing users in these cities. Full article

The rapid adoption of ridesourcing poses challenges for researchers and policymakers in the Middle East and North Africa (MENA), as it is an evolving new transport mode, and there is little research explaining its effects on mobility behaviors in this region. There is a concern that ridesourcing, which offers convenient and relatively cheap door to door services, encourages citizens to replace their sustainable travel modes, like walking, with car use. This effect has been studied relatively well in metropolises of the West, but less in the MENA agglomerations. This paper investigates whether regular use of ridesourcing impacts the walking mode choice in Cairo and Tehran. The analysis uses the results of 4926 face-to-face interviews in these two cities to compare the preference for using a vehicle instead of walking between regular users of ridesourcing and other motorized modes, including public bus, urban transit rails, private car, and traditional taxi. The findings indicate that in Cairo, the regular ridesourcing users are more likely than regular users of public transport to use a vehicle instead of walking inside their neighborhood. However, in both cities, ridesourcing users are less likely than regular private car users to replace walking by using vehicles. Full article

Car use in modern cities with a well-developed public transit is more sophisticated to explain only through hard factors such as sociodemographic characteristics. In cities, it is especially important to consider motives for car use. Therefore, we examined two modern cities with a high modal share of non-motorized modes and public transit to answer the question: How do the affective and instrumental motives influence car use in such cities? The used data set was collected in Berlin and San Francisco. To investigate the role of motives, we applied an ordered hybrid choice model (OHCM) with a probit kernel. Based on the OHCM we explained more than 14% of the overall heterogeneity and gave further insights to the decision-making process. The affective motive had a strong influence on car use frequency, whereby the instrumental aspects did not matter. Furthermore, an effect resulting from age could not be determined for the affective motives in these cities. Results suggest people are more likely to use cars for affective motives despite the city’s adversities. For these people it is difficult to achieve a shift to alternative means of transport. The only way to intervene here is through regulatory intervention. Full article

Analysis of travel mode choice is vital in policymaking and transportation planning to comprehend and forecast travel demands. Universities resemble major trip attraction hubs, with many students and faculty members living on campus or nearby. This study aims to investigate the effects of socioeconomic characteristics on the travel mode choice of university students. A nested ensemble approach with artificial neural networks (ANNs) was used to model the mode choice behavior. It was found that students generally prefer motorized modes (bus and car). A more detailed analysis revealed that teenage students (aged 17–19 years) had an approximately equal probability of selecting motorized and non-motorized modes. Graduate students revealed a higher tendency to select motorized modes compared with other students. The findings of this study demonstrate the need to promote non-motorized modes of transport among students, which is possible by providing favorable infrastructure for these modes. Full article