Non-motorized Travel Research Articles

Factors driving reduction in CO2 emissions from personal travel: A repeated cross-sectional analysis

This study identifies factors that drive the changes in CO2 emissions from personal travel in urban areas during 2006 and 2016 using repeated cross-sectional household travel surveys conducted in Seoul, Korea. We first estimated the daily travel CO2 emissions of every survey participant for each year based on travel itinerary records and estimated CO2 intensity for the travel mode. The results suggest that total CO2 emissions from personal travel in Seoul declined remarkably between 2006 and 2016, potentially due to reduced vehicle use, transition to non-motorized travel, improved vehicle technology, and eased traffic conditions. Two multilevel mixed-effect Tobit regression models were developed to identify the factors affecting CO2 emissions from personal travel for each year. The results suggest that some cohorts (e.g. males, 30s and 40s, small households, high-income groups, car owners) are likely to produce more CO2 emissions from personal travel consistently in 2006 and 2016. The changes in the estimated coefficients between the two years were also evaluated statistically, suggesting that some population groups (e.g. elderly, large households) are likely to reduce CO2 emissions from personal travel. The reduction in CO2 emissions by these groups may be correlated with continued investment in the public transportation system in Seoul. These findings provide an opportunity to gain a clear understanding of travel behavioral changes related to CO2 emissions from personal travel, with insights toward sustainable development.

Exploring the spatial relationship between urban built environment and green travel: An improved semi-parametric GWR approach

Public transit and non-motorized travel offer numerous environmental, social and public health benefits. Existing literature focuses on the correlation between the built environment and green travel in metropolitan centers, with limited studies on suburban new towns, essential for comprehensive urban expansion and sustainable development. This paper examines the effect of the built environment on public transit and non-motorized travel by using point of interest data and household economic data from Jiading District, Shanghai. An innovative framework is introduced, based on the geographically weighted regression model, which captures spatial heterogeneity while balancing the inequality and directionality of different areas in the transportation network. The results demonstrate that the proposed improved semi-parametric geographically weighted regression outperforms the traditional geographically weighted regression model in evaluation capabilities. A distinct spatial heterogeneity in travel patterns is identified between central and peripheral areas. The densely populated central zones exhibit a preference for intra-district travel, often opting for non-motorized modes due to their convenience and health benefits while peripheral areas serve as crucial connectors, facilitating long-distance travel within the metropolitan area through public transit and multimodal transportation. Regional transportation infrastructure construction and socioeconomic factors also affect the choice of green travel. The findings could provide theoretical references and policy implications to the development of green transportation and urban sustainability.

Car Ownership, Commute Distance, and Commute Mode Choice in the Dense Megacity of a Developing Country: The Direct and Indirect Role of the Built Environment

Despite much having been published about the effects of the built environment (BE) on urban travel in the developed world, few articles have so far been published based on studies using a megacity in a developing country. The paper addresses the existing gaps in research by conducting a study in Dhaka, one of the densest urban areas globally. An integrated framework based on the structural equation model and discrete choice model is used to examine how individual commute mode choice behavior is influenced by the BE, as mediated by car ownership and commute distance. Three BE features—population density, street connectivity, and job-to-household ratio—have a direct and total positive association with non-motorized transport use. Although being close to bus stops does not directly affect people’s choice to take non-motorized transport, it does promote non-motorized travel in an indirect way by decreasing car ownership and commute distance. Population density, job-to-household ratio, proximity to the nearest central business district, and bus stop proximity all have a positive direct and total impact on transit use, although larger employment densities directly support automobile use over transit. Understanding how the BE affects commute distance, car ownership, and mode choice is a useful reference for the development of practical measures to reduce demand for automobiles.

The Effect of Built Environment on Carbon Emission from Travel Behavior in the City center of Surabaya

Climate change is currently demanding attention due to global warming caused by increased Greenhouse Gas (GHG) emissions from the land transportation sector. The built environment is one of the factors that can influence non-motorized travel and emissions production. The built environment is identified using the 3D component approach (Density, Diversity, Design). The City center of Surabaya area is one of the Central Business Districts (CBDs) that is not exempt from emission issues and has the potential to decrease emissions through environmental arrangement scenarios. This research investigates the correlation between the 3D components and carbon emission production using the method of correlation analysis and multiple linear regression. The analysis results show a significant correlation between carbon emission production and the travel behavior of the city center of the Surabaya area community. Travel behavior variables influencing carbon emission production are household income, vehicles ownership, and travel distance. Furthermore, the study results indicate a significant relationship between travel behavior and the 3D components, as well as a significant association between the 3D components and carbon emission production. The 3D components that affect carbon emission production are population density, land use mix index, and pedestrian pathway density.

TOD effects on travel behavior: A synthesis of evidence from cross-sectional and longitudinal studies

The impacts of transit-oriented development (TOD) on travel behavior have been extensively studied, with a predominant focus on cross-sectional analyses that provide a static evaluation at a specific point in time by comparing TODs and non-TODs. Longitudinal assessments that capture changes in behavior over time remain relatively uncommon, and the literature tends to overlook differences in evaluating TOD effects across cross-sectional and longitudinal analyses. Additionally, the role of trip purpose as a significant but unexplored variable influencing the degree of TOD effects is often disregarded. To address these gaps, this systematic review examines 48 quantitative studies, comparing the effects of TOD on travel behavior from cross-sectional and longitudinal perspectives, restructuring indicators of effects into transit use, non-motorized travel, vehicle dependence, and vehicle ownership, and differentiating the effects by trip purpose. A metric has been introduced to quantitatively assess the impact of TOD on travel behavior. The pooled results indicate that private vehicle usage remains high in TOD areas, particularly for non-commuting trips, and that the longitudinal effects of TOD are limited and potentially influenced by individual travel attitudes, residential self-selection, and long-term travel habit change. Furthermore, the methodological differences between cross-sectional and longitudinal studies may lead to divergent conclusions regarding the effects of TOD on travel behavior. Our analysis sheds light on the importance of carefully selecting an appropriate method for a given research question to maximize the accuracy and relevance of the findings. Combining TOD and shared mobility can create a more efficient multi-model transport network that meets the diverse needs of city residents and improves accessibility for all. Overall, this review provides new insights into the impacts of TOD on travel behavior and supports the potential for a paradigm shift toward multimodal transport through the integration of TOD and shared mobility.

The nonlinear impact of cycling environment on bicycle distance: A perspective combining objective and perceptual dimensions

Extending cycling distances is crucial for sustainable urban transport development and plays a role in encouraging the shift from motorized vehicles to public transport. However, there is a lack of research examining the combined impacts of both objective and perceived aspects of the cycling environment on cycling distance, and the existence of threshold effects remains unclear. This study uses 2019 cycling data from Shenzhen, China, employing the XGBoost algorithm to uncover the relative importance and thresholds of objective and perceived factors in the cycling environment. The results indicate that population density (24.8%), road network density (15.2%), the proportion of recreational facilities (9.1%), perceived accessibility (8.0%), and comfort (8.6%) hold high relative importance in predicting cycling distance. Also, maintaining road network density between 3 to 6 km/km2 and increasing the population density to exceed 22,000 people/km2 proves effective in extending cycling distances. Land use demonstrates a threshold effect, with cycling distances increasing when the recreational facilities share exceeds 8%, transport facilities share remains below 25%, and commercial facilities share stays below 30%. Perceived metrics exhibit a clear threshold effect. The study identifies that perceived safety indicates a psychological bottleneck in increasing cycling distance. Perceived accessibility is positively correlated with cycling distance when accessibility is at a low level, while comfort shows a positive correlation with cycling distance when comfort is at a high level. These findings can contribute to refining land planning and prioritizing resource allocation for organizations aiming to promote non-motorized travel and design bicycle-friendly environments.

Evaluating Sustainable Mobility: Motorized and Non-motorized Modes in Suburban Areas of Thailand.

The impact of sustainable mobility may be considered through the mode of non-motorization, which may vary by spatial environmental conditions, especially accessibility by active mobility in a necessarily safe, comfortable, and pleasant atmosphere. Sustainable mobility comprises willingness to allow commuting behaviors on non-motorized travel for safety and green ethics. These chiefly participate to create livable places, improve health and well-being, and promote quality of life and economic growth. By contrast, an impoverished street environment decreases the possibility of active mobility and relies on motorization. It is essential to explore the perception of road users through diverse environmental features of street networks and sidewalks directly affecting road user behavior and preferences about motorization and non-motorization. This study evaluated multi-indicators of sustainable urban mobility (SUM) for a group of road users, with data gathered by questionnaire-based survey. 1,998 Pathum Thani Province inhabitants in Thailand were asked to evaluate their neighborhood characteristics by SUM indicators. In addition to different social dimensions and socioeconomic backgrounds, transportation characteristics among diverse trip makers and different perceptions of mobility indicators were classified by factor analysis using principal components and spatial visualization. Results were that traffic management, including intersection controls and signage, as well as information, sidewalk, management, and personal mobility choices, is a key strategy to improve sustainable mobility. This is achieved by tailoring solutions to achieve a modal shift prioritized over other supportive facilities.

The Lifestyle and Mobility Connection of Community Supported Agriculture (CSA) Users

Because Community Supported Agriculture (CSA) is considered a greener food shopping option, we hypothesized that users would adopt other sustainability-oriented behaviors. We sought, through 16 qualitative interviews with users, to understand whether CSA use in Montreal, Canada was accompanied by more ecological, non-motorized travel behavior. We find that urban users are typically able to subscribe to a nearby drop-off point and that many of them use non-motorized modes to access their weekly baskets. However, because of the weight of large produce baskets and the set time periods for collection, basket pick-ups by new Covid-19 era users are often part of car-based trip chains.

Wi-CL: Low-Cost WiFi-Based Detection System for Nonmotorized Traffic Travel Mode Classification

Traffic travel mode identification and classification are crucial for the development of intelligent transportation systems (ITSs). At present, scholars have investigated the classification of motorized and nonmotorized traffic travel in various road environments; however, the classification of walking and bicycle modes in nonmotorized travel has been largely ignored. Therefore, in this paper, we investigate nonmotorized traffic travel and propose a new low-cost nonmotorized traffic travel mode classification system, known as the Wi-Fi classification (Wi-CL) system that uses Wi-Fi signal detectors and the refined characteristics of nonmotorized travel modes. The Wi-CL system includes four modules: data acquisition module, data processing module, feature extraction module, and mode classification module. In the data acquisition module, the proposed system detects the Wi-Fi signals of traffic participants in road environments. In addition, we propose a received signal strength indicator (RSSI) filtering algorithm for hybrid traffic networks that effectively addresses surrounding obstacles and environmental noise. In the feature extraction module, we extract relevant traffic features to construct a mode classification model. Finally, a recurrent neural network (RNN) framework based on the long short-term memory (LSTM) algorithm is successfully implemented in the mode classification module for traffic travel mode identification. To validate the effectiveness of the Wi-CL system, extensive experiments were conducted using field data collected by Wi-Fi detectors installed at the South China University of Technology (SCUT). The experimental results show that the proposed RSSI filtering algorithm achieves excellent signal filtering results in real road traffic environments. In addition, the constructed travel speed estimation algorithm outperforms other baseline models in four different scenarios (flat-peak walking, midday peak walking, flat-peak cycling, and midday peak cycling), achieving an overall classification accuracy of 97.92%. In summary, our Wi-CL system is a feasible approach for nonmotorized traffic travel mode classification.

Explaining US travel behavior with perceived threat of pandemic, consumer sentiment, and economic policy uncertainty

Since the COVID-19 outbreak, consumer behavior has been affected by the perceived threat of the pandemic and economic uncertainty. This paper aims to explore the dynamic effects of COVID-19, consumer sentiment, economic policy uncertainty, and fuel prices on travel behavior in the United States. Using updated daily trip data, the results show that consumer sentiment has a positive long-run impact on travel demand for air and auto, suggesting that a positive change in consumer sentiment can boost demand for these modes of transportation in the long term. Additionally, consumer sentiment has a favorable effect (1.34) on demand for long-distance trips, but it has a negative impact (−0.42) on the number of people staying at home. Economic and political shocks have a detrimental impact on demand for air and auto travel, suggesting that consumers reduce the frequency and cost of these transport services if they have pessimistic expectations about the future state of the economy and policy. However, in the short term, US travelers appear to be insensitive to shocks in consumer sentiment and economic policy uncertainty. Regarding the perceived threat of the pandemic, the results indicate that rising COVID-19 cases have a negative long-term effect on demand for air travel (−0.09) and public transit (−0.19), while they are positively associated with demand for auto travel (0.06). Similarly, the increasing number of deaths due to COVID-19 has led to a shift from shared-use mass transportation (air travel and public transit) to private autos and non-motorized travel, such as walking in the short term.

Does density promote non-motorized travel? The effects of individual space–time activity patterns

Does density promote non-motorized travel? The effects of individual space–time activity patterns

Study on the Spatial and Temporal Distribution and Traffic Flow Parameters of Non-Motorized Vehicles on Highway Segments Crossing Small Towns

The traffic flow of non-motorized vehicles on the highway segments crossing small towns is disorderly and chaotic. In order to improve the traffic environment and regulate the order of non-motorized operations, this article studies the spatial and temporal distribution and traffic flow parameters of non-motorized traffic on the highway segments crossing small towns. The non-motorized traffic within the section of the National Highway G310 crossing small towns in Henan Province, China, is investigated through various research tools such as questionnaires, interviews, and on-site statistics. The regularity and characteristics of non-motorized traffic in terms of travel purpose, travel distance, travel time, and travel frequency were obtained. Meanwhile, based on the actual collected traffic data, the speed–density relationship, flow rate–density relationship, and speed–distance relationship of non-motorized traffic flow were studied using mathematical and statistical methods. The results show that thresholds exist for both time and distance traveled by non-motorized vehicles on small town road sections. The threshold value of riding time is 30 min, and the threshold value of riding distance is 5 km. Under the free flow state, the speed distribution is near a certain desired speed, and the flow rate–density relationship conforms to the exponential function relationship when the flow rate is greater than the critical flow value. The speed and distance show a cubic function relationship, and the speed gradually increases with the increase in distance between the non-motorized vehicles and towns. Based on the results of the above analysis, it is possible to grasp the travel regularity of non-motorized cyclists on highway segments crossing small towns. This provides a theoretical basis for enhancing the efficiency of non-motorized travel and improving the non-motorized travel environment.

Pedestrian Safety and Transit Corridors

This research examines the relationship between pedestrian accident locations on state-owned facilities (highways and urban arterials) and the presence of riders loading and alighting from bus transit. Many state facilities are important metropolitan transit corridors with large numbers of bus stops users, resulting in increased exposure of pedestrians to traffic and in increased numbers of collisions. The research also examines the association between pedestrian collisions and other travel generators (concentrations of retail activity and housing) as well as environmental conditions (wide roadways, high traffic volumes, and high speed limits).Based on a retrospective sampling approach and logistic regression models, the study shows that bus stop usage is associated with pedestrian collisions along state facilities. Less strong, but significant associations exist between retail location and size, traffic volume, and number of traffic lanes, and locations with high levels of pedestrian-vehicle collisions. The findings suggest that facilities with high numbers of bus riders need to accommodate people walking safely along and across the roadway. They support the development of state DOT programs for multimodal facilities, which integrate travel modes in major regional facilities within local suburban communities and pay specific attention to the role of transit in shaping the demand for nonmotorized travel on the facilities.

Pedestrian Environments and Transit Ridership

This paper explores how the quality of the pedestrian environment around transit stops relates with transit ridership. The primary hypothesis tested is that transit trip-making is higher in urban environments that are more conducive to non-motorized travel, given that bus transit systems are most frequently accessed via walking or biking. A secondary goal is to contribute to an improved understanding of the measurement of the built environment in geographic information systems (GIS). A composite measure of walkability—incorporating land use mix, density and street patterns—was developed for all transit stops in San Diego’s Metropolitan Transit Systems service area and used as a measure of the built environment. Findings indicate a small but significant, positive relationship between the walkability of the built environment and transit ridership.

Designing pandemic resilient cities: Exploring the impacts of the built environment on infection risk perception and subjective well-being

Since the beginning of the COVID-19 pandemic, authorities around the world explored ways to slowdown the spread of the disease while maintaining the physical and mental health of individuals. They redistributed the street space to promote physical activity and non-motorized travel while meeting the social distancing requirements. Although the statistics showed significant increases in walking and bicycling trips during the pandemic, we have limited knowledge about the associations between built environment characteristics, COVID-19 infection risk perception while traveling, and subjective well-being. This study assesses the impacts of the built environment on subjective well-being and infection risk perception while traveling during the pandemic. It uses data collected from the residents of Columbus, Ohio, through a multi-wave survey conducted at different time points during the COVID-19 outbreak. By employing a structural equation modeling approach, it explores the associations between residential neighborhood characteristics, individuals’ subjective well-being, and perceived infection risk while using non-motorized modes and shared micromobility. The findings show that those living in more compact, accessible, and walkable neighborhoods are less likely to perceive active travel and shared micromobility as risky in terms of COVID-19 infection. Our results also show that built environment characteristics have an indirect positive effect on the subjective well-being of individuals. The findings of our study demonstrate that built environment interventions can help promote physical activity and support mental health of individuals at this critical time. Our study also indicates that designing compact neighborhoods will be a crucial element of pandemic resilient cities in the post-COVID-19 era.

Factors impacting bike crash severity in urban areas

Introduction: Bicycling plays an important role as a major non-motorized travel mode in many urban areas. While increasingly serving as a key part of an integrated transportation demand management system and a sustainable mobility option, interest in biking as an active transportation mode has been unfortunately accompanied by an increase in the number of bike crashes, many with incapacitating injuries or fatal outcomes. Thus, to improve bicycling safety it is crucial to understand the critical factors that influence severe bicyclist crash outcomes, and to identify and prioritize policies and actions to mitigate these risks. Method: The study reported herein was conducted with this objective in mind. Our approach involves the use of classification models (logistic regression, decision tree and random forest), as well as techniques for treating unbalanced data by under sampling, oversampling, and weighted cost sensitivity (CS) learning, applied to bike crash data from the State of Tennessee’s two largest urban areas, Nashville and Memphis. Results: The results indicate that random forest with weighted CS offers the potential for greater explanatory accuracy, an important observation given the paucity of efforts to date in applying random forest to bike safety studies. Inadequate lighting conditions, crashes on roadways, speed limits, average annual daily traffic, number of lanes, and weekends are the critical features identified. Conclusion: Based on these results, a series of specific, suggested policy changes are presented for implementation consideration. Practical Applications: There is existing guidance in FHWA Lighting Handbook and TDOT’s Roadway Design Guidelines that spell out some engineering design solutions like lighting provisions, bicycle facility design, and traffic calming measures. These measures may alleviate the identified key features impacting fatal and incapacitating bicycle injuries. Further research should be conducted to gauge the efficacy of the solutions suggested.

Associations of inclement weather and poor air quality with non-motorized trail volumes

Inclement weather and poor air quality present unfavorable conditions for active transportation. This study investigated associations of area-wide air pollution and inclement weather with non-motorized travel at two trail locations in northern Utah (one recreational, one utilitarian). Rainfall, snowfall, high/low temperatures, and high wind speed were measures of inclement weather, and overall air quality index (AQI) measured air pollution. This study fit time-series (ARIMA) regression models for the utilitarian and recreational sites (separately), using multiple years of daily non-motorized counts. Non-motorized travel was reduced significantly during inclement weather, and the effects of rainfall and cold temperatures were smaller for the utilitarian site than for the recreational site. Counts at the recreational site were higher on holidays, weekends, and days with greater snow depth. Although non-motorized counts were lower on days with worse air quality at the utilitarian location, they were higher at the recreational location. Results are interpreted in terms of the discretionary nature of recreational travel, particulars of the location and elevation of the recreational site, and a desire to escape pollution through outdoor recreation in a natural area.

Health impacts of nonmotorized travel behavior and the built environment: Evidence from the 2017 National Household Travel Survey

IntroductionThe health impacts of nonmotorized travel behavior (i.e., walking and bicycling) and the built environment have been widely investigated in the past. However, nationwide studies probing the association between the neighborhood built environment and individuals' nonmotorized travel and health status remain scarce. Utilizing national-level databases from the U.S., the present study examines the role of nonmotorized travel and neighborhood-level built environment attributes in residents’ self-assessed overall health status. MethodsThis research relies on data from the 2017 National Household Travel Survey and employs ordered probit modeling techniques to relate self-assessed overall health status to various variables representing the extent of nonmotorized travel and physical activity as well as the built and social environment attributes of the residential location. ResultsThe results indicate that individuals’ self-assessed health status is associated with i) their person-level attributes including the levels of their walking and bicycling travel and other physical activity; ii) the social environment attributes of both their household and their residential neighborhood including income and vehicle ownership levels; and iii) the built environment attributes of their neighborhood including the extent of: compactness, mixed-use development, transit accessibility, and automobile-friendliness of the road network within the neighborhood. Furthermore, average marginal effects computations indicate that the most important neighborhood built environment factor in increasing the likelihood of reporting a very good or excellent health status is higher levels of transit accessibility. ConclusionsThe findings of this study suggest that more effective policies to promote individuals' health through changes targeting their travel behavior and their neighborhood's built environment may be the ones that: a) encourage walking and bicycling travel, and b) foster neighborhood designs that support more active lifestyles through increased levels of transit accessibility, activity density, and land use diversity, as well as through decreased levels of automobile-friendliness of the street network.

Generating Inclusive Health Benefits from Urban Green Spaces: An Empirical Study of Beijing Olympic Forest Park

Nature proximity contributes to improving many people’s health. In urban areas, how to increase the gaining of health benefits from urban green space (UGS) has gradually become a topic of concern for urban planners and public health practitioners. However, studies that can make causal inferences and evidence from developing countries and societies are still limited, and little has been done to address the issue of equity. Using data gathered in face-to-face surveys from 997 visitors to the Beijing Olympic Forest Park, we applied the instrumental variable approach to analyze park visit benefits to human physical and mental health, and explore ways that can help motivate visits and enhance equitable use. The results show that the overweight had more frequent visits, indicating that people with less-than-ideal health status might feel the urgency in improving their health and choose to engage in more recreation. In this sense, UGS showed a tendency to provide means for a certain group of people to proactively improve health. The study also solidifies the mid-term stress-relieving effect of park recreation that increased with visiting frequency, and found that visits to different types of UGS should all be beneficial, and do not have to be to large green parks. While distance is a decisive factor in encouraging UGS visits, route friendliness was found to have a complementing role, implying that creating routes to UGS that are more conducive to non-motorized travel (walking and cycling) could be seen as an important instrument. Both the incentives to park visitation and stress-relieving effect are more pronounced in elder groups, indicating higher potentials of such an approach in cities with an aging population. In terms of equity, differences in knowledge and income levels associated with use levels indicated inequitable use. To promote inclusiveness, the policy could start by increasing people’s recognition of health benefits derived from UGS recreation through for example publicity programs. The study could bring implications for planning practitioners to leverage health potentials from increased and equitable use of UGS. Limitations of the study includes: (1) given the nature of cross-sectional data, the possibility of two-way causality cannot be ruled out, and (2) the study was conducted in one park and only park visitors were surveyed. In the future, researchers could consider conducting multi-period surveys, and to look at the city level to include all kinds of recreational UGS, and, if possible, to cover all residents.

Adapting Urban Transport Planning to the COVID-19 Pandemic: An Integrated Fermatean Fuzzy Model.

The critical worldwide problem of adapting urban transport planning to COVID-19 is for the first time comprehensively addressed and solved in this study. It primarily aims to help transport planners increase the resilience of transport systems. Firstly, a multi-level decision-making hierarchy structure based on four main criteria and 17 sub-criteria is introduced for relevant stakeholders to provide a practical framework for assessing existing transport plans. Then, a three-stage integrated Fermatean fuzzy model for adapting urban transport planning to the pandemic is presented. The model hybridizes the method based on the removal effects of criteria (MEREC) and combined compromise solution (CoCoSo) method into a unique methodological framework under the Fermatean fuzzy environment. A case study provides decision-making guidelines on how to adapt transport plans to COVID-19 in the real-world context of Belgrade, Serbia. The research findings show that the pandemic significantly changed the priorities of transport planning strategies and measures. “Non-motorized travel” is now the best alternative since its numerous short-term measures lead to better transport service. The major advantages of the introduced model are higher flexibility and a more precise fusion of experts’ preference information. The integrated Fermatean fuzzy model could be used for adapting other emerging problems to COVID-19.