Why do bicyclists take detours? A multilevel regression model using smartphone GPS data

Bicyclists often deviate from the shortest possible routes and take detours in search of more pleasant riding conditions. The extent of detours and the factors affecting bicyclists to ride excess distances have not yet been fully explored. This study aims to measure and analyze the detour extent of utilitarian bicycle trips and their relationships with the route-level environmental components using data collected from individual bicyclists' smartphone GPS in Columbus, Ohio. Comparing the chosen routes with their shortest counterparts, we calculate two detour indices (a distanced-based index and an area-based index) and provide a comparative analysis of built environment attributes for low, moderate, and high levels of detours. We then estimate multilevel mixed-effect generalized linear regression models to identify the contribution of built-environment characteristics to such detours while accounting for individual heterogeneity. We find that most bicycle trips (91.1%) include a detour and are 13.5% longer on average than their shortest alternatives with large variations. Detour degrees are higher for long-distance trips and for peak-period trips. We find that bicyclists choose routes with smaller shares of commercial and single-family land-uses and low levels of land-use diversity. Longer detours are positively associated with street greenery. We find that sparse bicycle facilities and high-speed limits are strong contributors to bicyclists' detour decisions, while multilevel mixed-effect linear regression models further present significant heterogeneity in bicyclists' responses to some environmental attributes. The area-based detour index performs better in explaining the relationships between land-use features and detour degrees.     

Does bicycle network level of traffic stress (LTS) explain bicycle travel behavior? Mixed results from an Oregon case study

This paper draws on census data, mode choice, and regional household travel survey data to investigate the relationship between the varying levels of traffic stress (LTS) routes and bicycle travel behavior. Specifically, does bicycle level of traffic stress explain bicycle behavior enough to warrant its use given the limitations? Bicycle level of traffic stress is a new system of bicycle infrastructure classification for cities that cannot afford existing alternatives such as bicycle level of service which is more data-intensive. The LTS criteria lacks many of the input variables found significant in predicting both route choice and mode choice, including several that are required for its more expensive alternatives. The authors select the Salem-Keizer metropolitan area, a case study representative of the kinds of small and medium-sized communities that may prefer to use the LTS to evaluate cycling infrastructure given its lower input costs. The results validate LTS on travel behavior data from the Oregon Household Activity Survey (OHAS), but not mode choice data from the American Community Survey (ACS). These results suggest the LTS criteria may not be useful for cities looking to prioritize infrastructure improvements for specifically increasing commuter cycling. That said, results suggest the system provides a valid measure of a household's propensity to cycle. Further research on a broader cross-section of communities can clarify these mixed results.     

Route choice behavior in a radial structured urban network: Do people choose the orbital or the route through the city center?

We use a license plate survey to study route choice through the city center of a medium-sized Dutch city, in which car drivers can basically choose between the orbital and center ring. For a sample of 1397 trips, we fitted a multinomial logit regression model. According to this model, route choice is relatively little influenced by actual travel time. This corresponds with the fact that many drivers did not choose the shortest time route. Travel distance in combination with one “route type velocity” for all orbital routes, and one route type velocity for all center routes is the most decisive factor. The route type velocity indicates how fast and attractive routes of that type are (being perceived). The results support the hypothesis that orbital routes are more attractive as these routes avoid the busy city center. This effect is however partly offset by the fact that drivers also prefer routes in the direction of their destination. These direct routes are mainly center routes. The results show the importance of considering complete routes in relation to their location and not only as a set of links and nodes when modeling route choice.     

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.     

Modeling bicycle crash costs using big data: A grid-cell-based Tobit model with random parameters

Bicyclists are among the most vulnerable road users in the urban transportation system. It is critical to investigate the contributing factors to bicycle-related crashes and to identify the hotspots for efficient allocation of treatment resources. A grid-cell-based modeling framework was used to incorporate heterogeneous data sources and to explore the overall safety patterns of bicyclists in Manhattan, New York City. A random parameters (RP) Tobit model was developed in the Bayesian framework to correlate transportation, land use, and sociodemographic data with bicycle crash costs. It is worth mentioning that a new algorithm was proposed to estimate bicyclist-involved crash exposure using large-scale bicycle ridership data from 2014 to 2016 obtained from Citi Bike, which is the largest bicycle sharing program in the United States. The proposed RP Tobit model could deal with left-censored crash cost data and was found to outperform the Tobit model by accounting for the unobserved heterogeneity across neighborhoods. Results indicated that bicycle ridership, bicycle rack density, subway ridership, taxi trips, bus stop density, population, and ratio of population under 14 were positively associated with bicycle crash cost, whereas residential ratio and median age had a negative impact on bicycle crash cost. The RP Tobit model was used to estimate the cell-specific potential for safety improvement (PSI) for hotspot ranking. The advantages of using the RP Tobit crash cost model to capture PSI are that injury severity is considered by being converted into unit costs, and varying effects of certain explanatory variables are accounted for by incorporating random parameters. The cell-based hotspot identification method can provide a complete risk map for bicyclists with high resolution. Most locations with high PSIs either had unprotected bicycle lanes or were close to the access points to protected bicycle routes.     

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.     

Effects of upgrading to cycle highways - An analysis of demand induction,use patterns and satisfaction before and after

ObjectiveThe objective of the present study is to investigate the effects of improvements made to two large, interconnected bicycle infrastructure in the western suburbs of Copenhagen, Denmark, on bicycle volumes and mode share, and cyclists' behaviour, perceptions, and experiences.MethodsEffects are assessed by analysing data from automatic counting stations during 35 months to measure the changes in bicycle volumes on the investigated routes. Furthermore, a questionnaire survey repeated three times – before, and one and two years after opening the improved routes - is used. Findings are supported by a control survey at a nearby facility, which was not influenced by the infrastructure improvements.ResultsThe investments related to the two investigated cases of infrastructure improvements resulted in a significant increase in the volume of bicyclist two years after the improvements. On one of the routes, the “Albertslund Route”, on weekdays during the rush hour in daylight, an increase from 126 to 203 bicyclists/h was recorded, whereas an increase from 24 to 32 bicyclists/h was recorded at “Vestvolden” for the same period. Most of the increase could be attributed to relocation of bicyclists from other routes. Induced cycling trips – trips that were not previously made by bicycle - were estimated to account for only 4–5% of the bicyclists two years after improvements. Bicyclists using the improved route express an increase in satisfaction with the quality of the facilities, which is significantly higher than at the control site.ConclusionData from the counting stations provides useful information if measured over a long period. This is necessary to correct for factors such as climate effects and temporal variation. Investments in cycle infrastructure in the investigated case led to a higher number of bicyclists who were mainly relocated from other routes. A minor increase in the modal share of cyclists was observed two years after the infrastructure improvements. Furthermore, the investments resulted in a higher degree of satisfaction among active bicyclists. If measured over a longer period, this could lead to a higher modal share due to a potential social advertising effect.     

Street centrality and land use intensity in Baton Rouge, Louisiana

This paper examines the relationship between street centrality and land use intensity in Baton Rouge, Louisiana. Street centrality is calibrated in terms of a node’s closeness, betweenness and straightness on the road network. Land use intensity is measured by population (residential) and employment (business) densities in census tracts, respectively and combined. Two GIS-based methods are used to transform data sets of centrality (at network nodes) and densities (in census tracts) to one unit for correlation analysis. The kernel density estimation (KDE) converts both measures to raster pixels, and the floating catchment area (FCA) method computes average centrality values around census tracts. Results indicate that population and employment densities are highly correlated with street centrality values. Among the three centrality indices, closeness exhibits the highest correlation with land use densities, straightness the next and betweenness the last. This confirms that street centrality captures location advantage in a city and plays a crucial role in shaping the intraurban variation of land use intensity.     

Determination of active travel speed for minimum air pollution inhalation

A higher active travel speed has offsetting impacts on air pollution inhalation dose through higher breathing rate but shorter exposure duration. The net effect of speed choice on inhalation dose for pedestrians and bicyclists has not been established. This paper derives equations for pedestrian and bicycle steady-state minimum-dose speed (MDS). Parameter distributions from the literature are applied to a synthetic population of travelers to calculate individual MDS. Results strongly support the existence of a definable MDS, which is near observed travel speeds for urban pedestrians and bicyclists. For a wide range of travelers, the MDS is 2–6 km/h while walking and 12–20 km/h while bicycling, decreasing with road grade at a rate similar to observed speeds. On level ground, pedestrian and bicycle MDS corresponds to a moderate-intensity physical activity level (3–6 MET). Small deviations from the MDS have little effect, but large deviations (by more than 10 km/h for bicycling) can more than double inhalation dose over a fixed distance. It appears that pedestrians and bicyclists choose travel speeds that approximately minimize pollution inhalation dose, although pollution is unlikely a primary motivation.     

Investigating the associations between road network structure and non-motorist accidents

Road networks channel traffic flow and can impact the volume and proximity of walking and bicycling. Therefore, the structure of road networks—the pattern by which roads are connected—can affect the safety of non-motorized road users. To understand the impact of roads’ structural features on pedestrian and bicyclist safety, this study analyzes the associations between road network structure and non-motorist-involved crashes using data from 321 census tracts in Alameda County, California. Average geodesic distance, network betweenness centrality, and an overall clustering coefficient were calculated to quantify the structure of road networks. Three statistical models were developed using the geographically weighted regression (GWR) technique for the three structural factors, in addition to other zonal factors including traffic behavior, land use, transportation facility, and demographic features. The results indicate that longer average geodesic distance, higher network betweenness centrality, and a larger overall clustering coefficient were related to fewer non-motorist-involved accidents. Thus, results suggest that: (1) if a network is more highly centered on major roads, there will be fewer non-motorist-involved crashes; (2) a network with a greater average number of intersections on the shortest path connecting each pair of roads tends to experience fewer crashes involving pedestrians and bicyclists; and (3) the more clustered road networks are into several sub-core networks, the lower the non-motorist crash count. The three structural measurements can reflect the configuration of a network so that it can be used in other network analyses. More information about the types of road network structures that are conducive to non-motorist traffic safety can help to guide the design of new networks and the retrofitting of existing networks. The estimation results of GWR models explain the spatial heterogeneity of correlations between explanatory factors and non-motorist crashes, which can support regional agencies in establishing local safety policies.     

Random and fuzzy utility models for road route choice

The objective of this work is the specification, calibration and validation of a route choice model distinguishing the phases of set perception and route choice among the alternatives belonging to the chosen set. Route generation is performed with a selective multi-criteria approach; route choice is performed using random and fuzzy utility models. As regards the fuzzy utility models, a new specification of a fuzzy model is introduced. Random and fuzzy utility models are compared and applied on a test road network and were specified and calibrated for national road freight transport on a real Italy-wide network.     

The capability of Motorways of the Sea of being competitive against road transport. The case of the Italian mainland and Sicily

The paper deals with a quantitative analysis on the competitiveness of intermodal transport, based on Motorways of the Sea (MoS), in comparison with all-road transport. This analysis is applied to MoS routes connecting the Italian mainland with Sicily. The study involves: a detailed intermodal network model which compares monetary costs and travel times from all relevant origins in the mainland to all relevant destinations in Sicily; and a survey carried out at some representative trucking firms operating to/from Sicily. The aim of the interviews has been: on one side to make a comparison between the theoretical and actual mode of transport and routes taken, and in case they are different, to understand the reasons of the discrepancy; on the other side to determine what are the main aspects taken into account by trucking companies in their modal choice, in order to understand how the competiveness of MoS against road transport can be improved. The results of the analysis show that the modal choice is affected by several elements: monetary costs and travel times; reliability of MoS routes; availability of MoS routes; MoS routes frequencies; but it resulted from the interviews that monetary costs and travel times are the most important factors considered by trucking companies in the modal and route choice. This study has also shown that an improvement of MoS routes on the Italian Adriatic side is necessary: actually, a strong reason for which in Italy MoS traffic is still a small percentage of road traffic is the low number of MoS routes currently in operation and their low frequency. This analysis could help decision makers, and maritime operators, to efficiently invest in the improvement of MoS routes. Moreover, this analysis, developed for an Italian case study, can be applied to other European and Mediterranean scenarios.     

Shortest path or anchor-based route choice: a large-scale empirical analysis of minicab routing in London

Understanding and modelling route choice behaviour is central to predicting the formation and propagation of urban road congestion. Yet within conventional literature disagreements persist around the nature of route choice behaviour, and how it should be modelled. In this paper, both the shortest path and anchor-based perspectives on route choice behaviour are explored through an empirical analysis of nearly 700,000 minicab routes across London, United Kingdom. In the first set of analyses, the degree of similarity between observed routes and possible shortest paths is established. Shortest paths demonstrate poor performance in predicting both observed route choice and characteristics. The second stage of analysis explores the influence of specific urban features, named anchors, in route choice. These analyses show that certain features attract more route choices than would be expected were individuals choosing route based on cost minimisation alone. Instead, the results indicate that major urban features form the basis of route choice planning – being selected disproportionately more often, and causing asymmetry in route choice volumes by direction of travel. At a finer scale, decisions made at minor road features are furthermore demonstrated to influence routing patterns. The results indicate a need to revisit the basis of how routes are modelled, shifting from the shortest path perspective to a mechanism structured around urban features. In concluding, the main trends are synthesised within an initial framework for route choice modelling, and presents potential extensions of this research.     

Analysis of the impact of street-scale built environment design near metro stations on pedestrian and cyclist road segment choice: A stated choice experiment

The mismatch between the design of the micro-scale built environment around metro stations and pedestrian/cyclist preferences causes inconvenience and dissatisfaction. How to design streets near metro stations to provide a walking/biking friendly built environment is still a key question in promoting the use of metro systems. To identify which general attributes of the street-scale built environment are relevant for pedestrians/cyclists and increase walkability/cycle-ability, this paper reports the results of a stated choice experiment in which eight built environment attributes were systematically varied: street segment length, average number of building floors on both sides of the street, retail shops in frontage of streets, street crossing facilities for pedestrians/cyclists, width of sidewalks/bicycle paths, greenery, density of street lamps and crowdedness of pedestrian/cyclists to understand their influence on a road segment choice and preferences. A total of 803 respondents were recruited from Tianjin, China to complete the stated choice experiment through on-street face-to-face interviews. A multinomial logit model was estimated to unravel pedestrian/cyclist preferences using the stated choice data. The results indicate that pedestrians and cyclists have similar preferences for road segments with building lower than 6 floors, 50% retail shops in frontage, more greenery, lamps between 15 m and 30 m, more crossing facilities, wider sidewalk/bike lane and not crowded. These significant built environment attributes can be used in urban design projects with a walking/biking friendly built environment around a metro station.     

Route choice and residential environment: introducing liveability requirements in navigation systems in Flanders

Vehicle route planning and navigation systems aim to provide the most beneficial routes to their users while disregarding the impact on the liveability of the surrounding residential areas. Therefore, future integration of route choice behaviour by route planners and measures to improve liveability and safety standards should be pursued. The Spatial Plan for Flanders, which is the overarching spatial policy plan in the northern part of Belgium, determines a system of road categories aimed at optimising the liveability of sensitive areas, such as residential neighbourhoods or school precincts, without jeopardizing accessibility. This paper examines to what extent routes proposed by commercial route planners differ from more socially desirable routes that are guided by the policy principles of road categorisation in Flanders as proposed by the plan. Results show that commercial route-planners’ routes choose more often roads of the lowest category than socially acceptable. However, for some of the assessed connections, the socially desired alternative is a feasible route as well, which is not excessively increasing time consumption or distance travelled. It is concluded that the implementation of the prevailing road categorisation system in Flanders in routing algorithms has the potential to promote more sustainable route choices, while infrastructural measures that discourage cut-through traffic may help materialising the categorisation system.     

Commuting patterns and bicycle level of traffic stress (LTS): Insights from spatially aggregated data in Franklin County,Ohio

This study analyzes the relationships between bicycle network design and commute mode shares in Franklin County, Ohio. We first adopt the bicycle level of traffic stress (LTS) criteria to measure bicycle network design. Then, we develop a fractional multinomial logit model to simultaneously estimate the determinants of the shares of commuters who regularly go to work by carpooling, riding public transit, bicycling, and walking. The analytical results show that increasing the proportion of low-stress road segments (LTS 2) is statistically significant and positively associated with the share of bicycle commuters at the census block group level. However, we do not observe a significant relationship between the proportion of very low-stress road segments (LTS 1) and the share of bicycle commuters. This study suggests that social and cultural relevant factors are more important predictors of reductions in automobile use as compared to those factors representing road conditions.     

Association between the built environment and children's independent mobility: A meta-analytic review

Children's independent mobility (CIM) is considered as a determining criterion of child-friendly built environment (BE). Researchers have made a substantial effort to identify the characteristics of the BE that affect CIM and thereby to inform city policy to promote CIM. Although the findings from these studies are useful to inform context specific CIM policy, together they provide inconclusive results. This study made a first attempt to draw a generalised conclusion through a meta-analysis of existing knowledge base. The analysis was conducted using primary studies reporting 13 BE-CIM links and published between 1980 and 2016. Overall effect size (ES), directions, and consistency of each link were calculated, also stratified by contexts, using the reported results from the primary studies and based on a random effect model. The results show that four BE factors (dead-end street, % of residential land, % of commercial land, and residential location type) have a positive association with CIM; traffic volume has a neutral association; and the remaining eight factors (vehicular street width, road density, intersection density, major road proportion, land use mix, availability of recreational facilities, residential density, and distance to destination) have a negative association. Living in a dead end street was found to have the strongest positive ES (0.352), with moderate level of consistency across the primary studies. In contrast, land use mix has the strongest negative ES (− 0.212) but with the highest level of inconsistency. Both ESs and consistencies, however, vary between developing and developed country contexts. Diversity in contexts, research design, and measurement instruments across the primary studies contributed to the heterogeneous results. The findings of this research serve as a guide for practitioners and researcher alike to make an informed decision about the BE factors that consistently foster or hinder CIM in different contexts.     

Macro-level analysis of bicycle safety: Focusing on the characteristics of both crash location and residence

Over the last decade, bicycle ridership has been encouraged as a sustainable mode of transportation as it is economic and has less impact on the environment. Still, higher crash risk for bicyclists remains a deterrent for people to choose bicycling as their major mode of travel. As a first step in investigating bicycle safety, it is essential to identify not only the characteristics of the areas with the excessive number of bicycle crashes; but also those of the areas where crash-prone bicyclists reside. Therefore, this study aims to identify contributing factors for two subjects: (1) the number of bicycle crashes in the crash location's ZIP code and (2) the number of crash-involved bicyclists in their residence's ZIP. In order to achieve these objectives, a multivariate Bayesian Poisson lognormal CAR (conditional autoregressive) model was developed to identify the contributing factors for each subject. Regarding the model performance, the multivariate model outperformed its univariate counterpart in terms of DIC (deviance information criterion). Subsequently, hot zones for the two target zones were identified based on the modeling results. It is expected that practitioners are able to understand the contributing factors for bicycle crashes and identify hotspots from the results suggested in this study. In addition, they could implement safety countermeasures for the identified problematic locations to effectively reduce bicycle crashes.     

Driverless vehicles' potential influence on bicyclist facility preferences

Research in the field of autonomous vehicle technology focuses on its enhanced safety and convenience for vehicle occupants. The present paper seeks to establish a line of inquiry that addresses the implications of autonomous vehicle technology for nonmotorized road users, in the present case, bicyclists. Studies show that motorized traffic volume and speed affect nonmotorized agents' behavior and facility preference, but the degree to which this will apply to a driverless environment requires further study. We developed a stated-preference survey that had respondents select their preferred facility in a variety of hypothetical scenarios with and without the presence of driverless vehicles and on street types of varying motorized traffic volumes and speeds. A Random Parameters Logit Model is estimated to analyze the links between facility preferences, sociodemographics, street types, and the existence of driverless vehicles. The model results suggest that increases in motorized traffic volumes and speeds correlate with a greater preference for separated facilities. The presence of driverless vehicles amplifies this preference. Controlling for other factors, under driverless vehicle conditions, the odds of selecting protected facilities, such as buffered bicycle lanes and cycle tracks, were more than double the odds found under current conditions. We conclude with recommendations for infrastructure and policy and suggestions for future research in this nascent field of study.     

Using spatial network analysis to model pedal cycle flows,risk and mode choice

Spatial network analysis (SpNA) provides a promising alternative to traditional transport models for the modelling of active travel, because walking and cycling behaviour is influenced by features smaller than the scale of zones in a traditional model. There is currently a need for link-level, city wide modelling of cycling, both to ensure the needs of existing cyclists are catered for in planning, and to model the effects of changing infrastructure in shaping cyclist behaviour. Existing SpNA models treat cyclists and car drivers as if they make navigational decisions in a similar way, which in reality is not the case.This paper presents an SpNA model using hybrid betweenness, which fits cyclist flows in Cardiff, Wales using distance, angular distance, motor vehicle traffic and slope as predictors of route choice. SpNA betweenness is also shown to implicitly capture the effect of urban density on mode choice. As it handles route finding decisions of drivers and cyclists separately, the model presented is also applicable to road safety models examining the interaction between the two classes of road user. The model has low cost of data collection and is reproducible using publicly available network analysis software and open mapping data. Further avenues for modelling the effect of infrastructure on cycling are discussed.