The geography of freight-related accidents in the era of E-commerce: Evidence from the Los Angeles metropolitan area

Due to the burgeoning demand for freight movement in the era of e-commerce, freight related road safety threats have been growing in both urban and suburban areas, despite the improved general traffic safety over the past decades. The empirical evidence on how freight trucks related crashes are distributed across neighborhoods and correlated to spatially varying factors is, however, highly limited. This article uses data from the Los Angeles region in 2018 to analyze the spatial patterns of freight trucks related traffic crashes and examines the major factors that contribute to those patterns using spatial econometric models. Maps show that freight trucks related crashes are highly associated with major freight generators but less clustered than the overall traffic crashes. Results from the spatial Durbin model indicate that access to freight generators, economic attributes, land uses, road infrastructure, and road network variables all contribute to the spatial distribution of freight trucks related crashes. The findings could help transport planners understand the dynamics of freight trucks related traffic safety and develop operational measures for mitigating the impacts of growing goods movement on local communities.     

Modeling AADT on local functionally classified roads using land use,road density,and nearest nonlocal road data

The focus of this research is to model the influence of road, socioeconomic, and land-use characteristics on local road annual average daily traffic (AADT) and assess the model's predictability in non-covered location AADT estimation. Traditional ordinary least square (OLS) regression and geographically weighted regression (GWR) methods were explored to estimate AADT on local roads. Ten spatially distributed counties were considered for county-level analysis and modeling. The results indicate that road density, AADT at the nearest nonlocal road, and land use variables have a significant influence on local road AADT. The GWR model is found to be better at estimating the AADT than the OLS regression model. The developed county-level models were used for estimating AADT at non-covered locations in each county. The methodology, findings, and the AADT estimates at non-covered locations can be used to plan, design, build, and maintain the local roads in addition to meeting reporting requirements. The prediction error is found to be higher at urban areas and in counties with a smaller number of local road traffic count stations. Recommendations are made to account for influencing factors and enhance the local road count-based AADT sampling methodology.     

Measuring cycling accessibility in metropolitan areas

Different measures of cycling accessibility have been widely introduced in transportation planning. However, those measurements are mainly restricted by the availability of travel behavior data. In addition, there has been limited comprehensive research on the importance of cycling accessibility to destinations based on the travel time or distance. In this paper, a new index for measuring bikeability in metropolitan areas is introduced. A Cycling Accessibility Index (CAI) is developed for quantifying cycling accessibility within local areas in metropolitan Melbourne, Australia. CAI is defined according to gravity-based measures of accessibility. This index measures cycling accessibility levels in terms of diversity of different land uses, number of activities in statistical areas, and the travel impedance between origins and destinations. The Victorian Integrated Survey of Travel and Activity (VISTA) dataset was used to evaluate the index and investigate the association between the cycling accessibility levels and the number of bicycle trips in local areas. The index is assessed by investigating the association between levels of cycling accessibility and the number of bicycle trips in statistical areas. Key findings indicate that there is a significant positive association between bike trips and the CAI.     

Exploring the impact of built environment factors on the use of public bikes at bike stations: Case study in Zhongshan,China

Many countries have implemented public bike systems to promote sustainable public transportation. Despite the rapid development of such systems, few studies have investigated how built environment factors affect the use of public bikes at station level using trip data, taking account of the spatial correlation between nearby stations. Built environment factors are strongly associated with travel demand and play an important role in the success of public bike systems. Using trip data from Zhongshan's public bike system, this paper employed a multiple linear regression model to examine the influence of built environment variables on trip demand as well as on the ratio of demand to supply (D/S) at bike stations. It also considered the spatial correlations of PBS usage between nearby stations, using the spatial weighted matrix. These built environment variables mainly refer to station attributes and accessibility, cycling infrastructure, public transport facilities, and land use characteristics. Generally, we found that both trip demand and the ratio of demand to supply at bike stations were positively influenced by population density, length of bike lanes and branch roads, and diverse land-use types near the station, and were negatively influenced by the distance to city center and the number of other nearby stations. However, public transport facilities do not show a significant impact on both demand and D/S at stations, which might be attributed to local modal split. We also found that the PBS usage at stations is positively associated with usage at nearby stations. Model results also suggest that adding a new station (with empty capacity) within a 300 m catchment of a station to share the capacity of the bike station can improve the demand-supply ratio at the station. Referring to both trip demand models and D/S models, regression fits were quite strong with larger R² for weekdays than for weekends and holidays, and for morning and evening peak hours than for off-peak hours. These quantitative analyses and findings can be beneficial to urban planners and operators to improve the demand and turnover of public bikes at bike stations, and to expand or build public bike systems in the future.     

How could the station-based bike sharing system and the free-floating bike sharing system be coordinated?

The station-based bike sharing system (SBBSS) and the free-floating bike sharing system (FFBSS) have been adopted on a large scale in China. However, the overlap between the services provided by these two systems often makes bike sharing inefficient. By comparing the factors that affect the usage of the two systems, this paper aims to propose appropriate strategies to promote their coordinated development. Using data collected in Nanjing, a predictive model is built to determine which system is more suitable at a given location. The influences of infrastructure, demand distribution, and land use attributes at the station level are examined via the support vector machine (SVM) approach. Our results show that the SBBSS tends to be favored in areas where there is a high concentration of travel demand, and close proximity to metro stations and commercial properties, whereas locations with a higher density of major roads and residential properties are associated with more frequent use of the FFBSS. With regard to the methods used, a comparison of several machine learning approaches shows that the SVM has the best predictive performance. Our findings could be used to help policy makers and transportation planners to optimize the deployment and redistribution of docked and dockless bikes.     

Annual average daily traffic estimation in England and Wales: An application of clustering and regression modelling

Collection of Annual Average Daily Traffic (AADT) is of major importance for a number of applications in road transport urban and environmental studies. However, traffic measurements are undertaken only for a part of the road network with minor roads usually excluded. This paper suggests a methodology to estimate AADT in England and Wales applicable across the full road network, so that traffic for both major and minor roads can be approximated. This is achieved by consolidating clustering and regression modelling and using a comprehensive set of variables related to roadway, socioeconomic and land use characteristics. The methodological output reveals traffic patterns across urban and rural areas as well as produces accurate results for all road classes. Support Vector Regression (SVR) and Random Forest (RF) are found to outperform the traditional Linear Regression, although the findings suggest that data clustering is key for significant reduction in prediction errors.     

Children aged 9-14 living in disadvantaged areas in England: Opportunities and barriers for cycling

The aim of this paper is to take a holistic perspective to explore levels of cycling and opportunities and barriers to increase children’s safer cycling in disadvantaged areas in England. The study was one part of a larger study which explored the factors underlying the high level of road traffic casualties especially among children in the most disadvantaged areas of England and to explore how this impacts on mobility and quality of life. The methods involved a cross sectional survey comprising school based questionnaire surveys with children aged 9-14 and focus groups with parents who had children within this age range. The surveys were conducted in 2007 and the focus groups during 2008. 4286 children completed the survey and eight focus groups were held. Bike ownership (77%) was high, use in previous week moderate (39%) but only 2% cycled to school. Ownership was significantly lower in minority ethnic groups. Despite young children’s strong preference to travel by cycle (30%) than walk or go by car, most parents felt it was too hazardous. It is unlikely that these findings would be any different from the rest of England, however the combination of environmental and social factors may elevate the risks for young cyclists in these areas. This paper concludes that a number of barriers exist to increasing levels of cycling among children living in disadvantaged areas particularly amongst ethnic groups. These barriers could be addressed by environmental modifications to reduce speeds and by reducing the levels of antisocial driving and riding in residential areas and around destinations where children travel, by providing cycle training to improve children’s skills and parent’s confidence, and by providing secure storage facilities for bikes. Until these barriers are addressed it is unlikely that cycling will increase despite the strong preferences children have to travel by bike. Such preferences to cycle provide an opportunity for local authorities to act on.     

Route choice of bike share users: Leveraging GPS data to derive choice sets

To identify the determinants of bike share users' route choices, this research collects 132,397 hub-to-hub global positioning system (GPS) trajectories over a 12-month period between April 1, 2015 and March 31, 2016 from 750 bicycles provided by Hamilton Bike Share (HBS). A GIS-based map-matching algorithm is used to derive users' routes along the cycling network within Hamilton, Ontario and generate multiple attributes for each route, such as route distance, route directness, average distance between intersections, and the number of turns, intersections, and unique road segments. Concerning route choice analysis, the origin and destination pair should be the same for all routes within a choice set, thus HBS users' trips are grouped by origin-destination hub pairs. Since trips taken by different users between a hub pair can follow the same route, unique routes are extracted using a link signature extraction tool. Following this, a normalized Gini (G_n) coefficient is calculated for each hub pair to evaluate users' preferences among all the unique hub-to-hub route choices. A G_n closer to 0 indicates that routes between a hub pair are more evenly used, while a value closer to 1 implies a higher preference toward one dominant route. Three route choice models, a global model, a medium G_n model, and a high G_n model, are estimated using Path-Size Logit to determine how route choice is affected by the presence of dominant routes. These models suggest that HBS users are willing to detour for some attributes, such as bicycle facilities, but tend to avoid circuitous routes, turns, steep slopes, and roads with high traffic volume.     

Estimation and validation of hybrid choice models to identify the role of perception in the choice to cycle

Cycling is one of the most sustainable and ecofriendly modes of travel and a good form of exercise. Many government and public health authorities recommend cycling to stay fit as well as to reduce air and noise pollution, CO₂ emissions, traffic congestion, and other negative consequences of car use. In light of these benefits, a major challenge for researchers today is how to promote cycling. However, in countries where cycling is not common, apart from the need for proper cycling facilities, one major issue concerns people’s perception of cycling for sport or recreational activities rather than as a mode of transport. The aim of this paper is to explore the role of perception in the likelihood of the bike being used for utilitarian purposes. We focus on the perception of: the bicycle as a means of transport; bikeability (in terms of usefulness and safety) and of bike infrastructure. Hybrid Choice Models (HCMs) have been used to estimate the effect of people’s perception on the propensity to bike. The HCM also accounts for the serial correlation between error terms in the discrete and latent perceptions, to allow for agent-common unknown factors. Furthermore, we also validate the model results using a hold-out sample and discuss some policy measures aimed at changing travel behavior. The results suggest that, besides individual characteristics, latent aspects related to the perception of the context and of the bicycle as a means of transport strongly affect the propensity to cycle.     

The impacts of the built environment on bicycle-metro transfer trips: A new method to delineate metro catchment area based on people's actual cycling space

Bicycle-metro integration is an efficient method of solving the “last mile” issue around metro stations. Built environment is believed to have a significant effect on cycling behavior. However, transfer cycling around metro stations, as a specific type of cycling behavior, has often been overlooked in transport research. In addition, static contextual units such as circular or street-network buffers are typically used to delineate metro catchment areas of transfer cycling trips. These methods are inaccurate to represent the actual geographic contexts of cycling trips, according to the uncertain geographic context problem (UGCoP). Thus, in this study, bicycle-metro catchment areas are delineated based on aggregating the end points of over three million transfer cycling trips. The impact of the built environment on transfer cycling behavior is also explored.First, we find that the aggregate-points buffer outperforms traditional static buffers in predicting transfer cycling trips. Second, we also identify a high level of spatial heterogeneity in catchment area and transfer cycling density between urban and suburban areas. Third, residential and working population density, bus stop density, and metro stations accessibility have a significant effect on bicycle-metro transfer cycling.     

How to promote sustainable public bike system from a psychological perspective?

The public bike system (PBS) has been actively promoted worldwide for the last decade. This study tried to find out policy strategies for sustainable PBS implementation targeting on the city that is under consideration of introducing bike sharing scheme. For this, the authors considered some psychological factors that may make impacts on PBS user's attitudes and hypothesized especially that individual environmental concern refers to an attitude toward environmental issues, influence an increase of their perceived value of PBS. The Norm Activation Model (NAM) is used to measure the public's environmental concern incorporating norm activation. In addition, willingness to pay (WTP) method is adopted to investigate the value of PBS individuals. Structural equation modeling (SEM) revealed that environmental concern influenced people's perception of the value of PBS. Furthermore, the positive correlation between environmental concern and awareness of consequences on cycling is observed. The study verifies how people perceive the value of a bike sharing system and how often people using a bicycle are dependent on their environmental concern. In conclusion, authors discuss how PBS could be promoted sustainably by suggesting policy strategies to enhance the perceived value of PBS and to increase bicycle use.     

From Mobike to no bike in Greater Manchester: Using the capabilities approach to explore Europe's first wave of dockless bike share

Globally, bike share schemes are an element of a rapidly changing urban transport landscape. Whilst many docked schemes are now embedded in cities around the world, the recent explosion of dockless systems provides an opportunity to evaluate claims that this form of shared mobility has the potential to alleviate common barriers to cycling, relieve congestion, boost low carbon travel, get people active, and reduce social exclusion. Drawing on a mixed methods study of 2270 online survey respondents and 27 interviews, all living in, working in or visiting Greater Manchester during a trial of dockless bike share, we explore the ways in which the technological, spatial and practical configuration of bike share schemes relate to a city's infrastructure and existing cycling practices. We question assertions that bike share provision necessarily results in increased rates of cycling and enhanced social inclusion.By using a capabilities approach and utlilising the concept of ‘conversion factors’ to describe the differing capacities or opportunities that people have to convert resources at their disposal into ‘capabilities’ or ‘functionings’, we show how the practice of bike sharing can influence a population's propensity to cycle, as well as how bike share interacts with established barriers to cycling. We find that many established barriers to cycling remain relevant, especially environmental factors, and that bike share creates its own additional challenges.We conclude that bike share operators must recognise the role of personal and social conversion factors more explicitly and be sensitive to the social and physical geography of cities, rather than assuming that a ‘one size fits all’ approach is adequate. To do this they should engage more closely with existing bodies, including transport authorities and local authorities, in co-creating bike share systems. Using the capabilities approach enables us to identify ways in which it could be made relevant and accessible to a more diverse population.     

Space-time demand cube for spatial-temporal coverage optimization model of shared bicycle system: A study using big bike GPS data

As a sustainable transport mode, bicycle sharing is increasingly popular and the number of bike-sharing services has grown significantly worldwide in recent years. The locational configuration of bike-sharing stations is a basic issue and an accurate assessment of demand for service is a fundamental element in location modeling. However, demand in conventional location-based models is often treated as temporally invariant or originated from spatially fixed population centers. The neglect of the temporal and spatial dynamics in current demand representations may lead to considerable discrepancies between actual and modeled demand, which may in turn lead to solutions that are far from optimal. Bike demand distribution varies in space and time in a highly complex manner due to the complexity of urban travel. To generate better results, this study proposed a space-time demand cube framework to represent and capture the fine-grained spatiotemporal variations in bike demand using a large shared bicycle GPS dataset in the “China Optics Valley” in Wuhan, China. Then, a more spatially and temporally accurate coverage model that maximizes the space-time demand coverage and minimizes the distance between riders and bike stations is built for facilitating bike stations location optimization. The results show that the space-time demand cube framework can finely represent the spatiotemporal dynamics of user demand. Compared with conventional models, the proposed model can better cover the dynamic needs of users and yields ‘better’ configuration in meeting real-world bike riding needs.     

Spatial and temporal analysis of shared bicycle use in Limassol,Cyprus

Cities around the world are moving away from the car-centric infrastructure, urban design and planning policies prevalent since the 1950s and promoting sustainable mobility as an alternative, including cycling. As such, Bicycle Sharing Systems (BSS) have emerged as a transport innovation across the globe. Cycling modal share however remains low in most Southern European island cities. These cities exhibit certain characteristics considered as barriers to cycling, such as hot summers and high humidity, hilliness, and car-oriented culture and infrastructure. Despite this, BSS and policies promoting cycling have emerged in this region as well. These have the potential to provide alternatives for those marginalized by car-based mobility and to reduce traffic related diseases and injuries, noise and air pollution, which can contribute to an improved quality of life for all citizens. Using the Mediterranean island city of Limassol (Cyprus) as a case study, the utilization of bicycle sharing is investigated by constructing regression models to assess the influence of spatial and temporal factors on the demand for BSS use at stations. From the regression models it appears that land use factors such as residential, commercial and park land use, as well as the presence of the beach and cycling paths positively influences frequency of use, as does higher network connectivity. While higher tourist arrivals have a positive effect, the presence of hotels in a 300 m buffer around the stations does not. Higher rainfall, as well as higher temperatures, are associated with a decrease in BSS use. Explicitly incorporating spatial dependence, in Spatial Auto-Regressive (SAR) models, led to the formulation of models with comparable or better explanatory power, when compared to the Ordinary Least Squares (OLS) models. The insights from the regression models can be used to inform policies promoting cycling and the design and planning of BSS (expansion) in Limassol and other cities.     

Road environments and bicyclist route choice: The cases of Davis and San Francisco,CA

Examining bicyclists' route choices provides valuable insights into the importance of road environments for bicycling. In this study, we examine the role of road factors, individual factors, and preference heterogeneity on route choice using two diverse and extreme cases in the U.S. The first case is bicycling to the University of California, Davis campus by students, faculty, and staff. This case represents the most bike friendly environment in the U.S. which affords a diverse bicycling population. The second case is bicycling to many destinations for many purposes in San Francisco, CA. It is more representative of a large U.S. city, but also has a relatively large bicycling mode share. It serves as an important case for examining the new innovative type of bicycling infrastructure that has been installed in North American cities over the past decade. Results suggest substantial within-city between-person heterogeneity in preference for road attributes and bicycling facilities as well as differences between contexts. Davisites show strong preferences for bike lanes and off-street paths and consistently choose routes of similar length to shortest routes indicating the need for suitable routes with minimal detours to support a large bicycling mode share. San Franciscans show strong preferences for conventional bike lanes on minor arterials, even stronger preferences for separated and protected bicycling facilities, and are willing to detour considerable distances to ride on them. Given large between-person differences within cities, we suggest usual valuations of bicycling facilities from elasticities and marginal rates of substitutions at the mean may need rethinking when applied to bike infrastructure planning.     

The role of psychological and socio-demographical factors for electric bike use in Norway

The electric bike (e-bike) is emerging as a new sustainable transport mode in Norway and has the potential to lead to increased cycling among the population. However, little is known about psychosocial determinants of e-bike use. The aim of the study was to examine the role of normative and environmental beliefs, the perceived attributes of e-bikes, and innovativeness and demographical factors related to e-bike use in a Norwegian sample. An online survey was used to collect data from 910 respondents, including both e-bike users (252) and non-users (658). The respondents were recruited via a commercial panel (response rate 42.04%) and a Facebook post. A structural equation modeling analysis was used to analyze the data. The structural model had a good fit to the data. The results showed that attitudes towards e-bike use followed by innovativeness were the most important predictors of e-bike use. The normative processes measured within the Norm Activation Model activated positive attitudes towards e-bike use, which in turn predicted e-bike use. There was a negative relationship between e-bike and conventional bike use, while a positive relationship was found between car and e-bike use. The results are discussed with regard to their implications for interventions aiming to promote e-bike use.     

The impact of carbon emission fees on passenger demand and air fares: A game theoretic approach

The implementation of an environmental market-based measure on U.S. aviation industry is studied. Under this policy, each airline pays a carbon fee for the carbon dioxide emissions it generates. The impact on ticket prices and corresponding market shares is investigated via the joint estimation of an air travel demand model and an airlines' behavior model. In the demand model, aggregate air traffic data is used to determine the marginal effects of flight attributes that are specific to itinerary, airline and airport on market share. The airline's behavior model incorporates the carbon fee in the airline marginal cost. After the implementation of the carbon policy, the increased cost forces airlines to adjust ticket prices in order to maximize profits. The results obtained by the proposed model indicate a moderate price increase which strongly depends on the per tonne carbon price. Air travel demand falls from 2.4% to 21% depending on the carbon price level.     

An intersectional analysis of barriers to cycling for marginalized communities in a cycling-friendly French City

In French cities like Grenoble, cycling is an increasingly popular form of urban mobility. Yet a lack of disaggregated modal data makes it unclear who does and who does not have access to biking. An intersectional analysis of 19 narrative and semi-structured interviews with policymakers, residents with different identities, and bike service providers demonstrate that some people perceive unique barriers to biking, related to their identities. For example, this study finds that racism, financial precarity, a lack of accessible information about services, and spatial inequalities may prevent some people from biking despite Grenoble's advanced cycling infrastructure and services and that these barriers may compound for certain people, reinforcing the preliminary body of research on intersectional barriers to urban biking. The study further finds that the lack of disaggregated demographic data on urban mobility in France might be preventing inclusive bike policy. The paper concludes by arguing that local policymakers would benefit from applying an intersectional analysis in understanding who is and is not biking, to promote everyday biking in a more inclusive way.     

Recreational bicycling as a “gateway” to utility bicycling: The case of Charlotte,NC

Riding a bicycle for utility purposes in US cities is rare, especially in historically automobile-dominated cities. Using data from a transportation survey administered to 406 residents of Charlotte, NC, this paper reports on the results of a logistic regression model that predicts the influence of an individual's recreational cycling frequency on the odds of that individual riding a bicycle for utility purposes on a weekly basis. The odds of an individual riding for utility purposes at least once a week increases dramatically as an individual rides more for recreation. Recreational cycling appears to offer a space in which individuals can acquire a threshold level of skills and materials necessary to ride their bike for utility purposes. Results suggest that plans to increase utility cycling in an automobile-dominated city like Charlotte ought to emphasize and fund opportunities for residents to ride recreationally, and consider how experience riding a bike in the temporally- and spatially- flexible context of recreation can encourage more individuals to ride to and from errands, school, or their place of work.