Associations of built environments with spatiotemporal patterns of public bicycle use

This study examines the association of built environment attributes with spatiotemporal patterns of public bicycle use. The study observations are based on the rental records of YouBike, which is a public bicycle system (PBS) in Taipei Metropolitan Area, Taiwan, from July to December 2015. A hierarchical clustering method is applied to identify the spatiotemporal patterns of YouBike use, and multinomial logit regression is used to clarify the associations. Empirical results indicate that the spatiotemporal patterns of PBS utilization differ in weekdays and holidays and are associated with locations, land use and facilities in a city. The empirical evidence fills the knowledge gap on the factors associated with the spatiotemporal patterns of PBS use and provides system operators with a valuable basis for the integrated planning and management of PBSs and built environments.     

Public bicycle as a feeder mode to rail transit in China: The role of gender,age, income,trip purpose,and bicycle theft experience

A marriage between public bicycle and rail transit presents new opportunities for sustainable transportation in Chinese cities. To examine determinants of public bicycle usage for rail transit access, an intercept survey of feeder mode choice among rail transit users was conducted near rail stations in Nanjing, China. Mode choice models were estimated with five feeder mode alternatives, including car, bus, walk, private bike, and public bike. By differentiating between public and private bicycle modes in the mode choice models, the study reveals the effects of personal demographics, trip characteristics, and station environments on public bicycle usage for rail transit access. Results show that female, older, and low-income rail commuters are less likely to use public bicycle to access rail transit. Rail commuters with bicycle theft experience and making school- or work-related trips are more likely to use public bicycle to access rail transit. Land use variables are largely insignificant in this study except that density shows a positive relationship with walking to rail transit. The results on demographic differences raise equity concerns when it comes to investing in public bicycle systems. Policy implications are discussed for Chinese cities to equitably boost public bicycle integration with rail transit.     

Land use effects on bicycle ridership: a framework for state planning agencies

State and local agencies increasingly recognize the importance of bicycling activity and as the number of riders has grown over the past several years, the agencies are becoming more aware of the need to provide better bicycle infrastructure. This paper proposes a series of empirical models and applies them to the State of Maryland in the United States, using a spatial lag approach to explore land use, built environment, demographic, socio-economic, and traffic condition connections to bicycle ridership, defined as the number of bicycle trips generated by a given analysis zone per day. A set of models is proposed for three land-use typologies: urban, sub-urban and rural. The data that drives this analysis was obtained from a recently conducted Household Travel Survey (HTS) in the Baltimore–Washington region in Maryland. Results show that some land uses, socio-economic and demographic characteristics, and transit accessibility are positively correlated with bicycle ridership. Other types of land use, transport system characteristics and income level have an inverse relationship with bicycle ridership. The contributing factors to bicycle ridership vary with land-use typology. This proposed approach could be used to evaluate factors relevant to bicycle demand. State and local agencies are advised to build designated bicycle paths according to traffic conditions and increase bicycle-parking capacity in specific establishments.     

Adaptive large neighborhood search heuristics for the vehicle routing problem with stochastic demands and weight-related cost

The vehicle routing problem (VRP) with stochastic demands and weight-related cost is an extension of the VRP. Although some researchers have studied the VRP with either stochastic demands or weight-related cost, the literature on this problem is quite limited. We adopt the a priori optimization to tackle this problem and propose a dynamic programming to compute the expected cost of each route. We develop the adaptive large neighborhood search heuristics equipped with several approximate methods for the problem. To evaluate our heuristics, we generate 84 test instances. Computational results demonstrate the performance of our heuristics and can serve as benchmarks for future researchers.     

Environmental correlates of dock-less shared bicycle trip origins and destinations

It remains unclear whether environmental conditions, including bicycle lane provision, are factors in dock-less bicycle uptake at the decision point. In conditions of generous bicycle supply and the ability to park a bicycle almost anywhere, it is important to know what local conditions might be influential in its use. The present study considered the role of 13 environmental factors in uptake and deposit rates by street segment, in a sample from a database of more than 3 m records over a ten-day period in May 2017, in Beijing, China. Street segments, defined by intersections, were coded for environmental factors using satellite and street view imagery. Uptake and especially deposit rates vary greatly by street segment and by local area. Linear regression reveals that 6 factors are highly significant in higher uptake and deposit – the presence of a dedicated bicycle pathway, street lighting, sidewalks, bicycle parking lots, POI density, and street segment length – while 3 were highly significant in lower uptake rates – interruptions in bicycle lane continuity, distance to the metro station, and elaborate environmental design. Altogether, environmental factors account for 42.4% of the variance in local bicycle trip origins and 38.7% of variance in destinations on street segments. This research contributes new understanding of the important role of local environmental factors in higher bicycle mode share in the context of a dock-less rental bicycle 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.     

Predictive spatial network analysis for high-resolution transport modeling,applied to cyclist flows,mode choice,and targeting investment

Betweenness is a measure long used in spatial network analysis (SpNA) to predict flows of pedestrians and vehicles, and more recently in public health research. We improve on this approach with a methodology for combining multiple betweenness computations using cross-validated ridge regression to create wide-scale, high-resolution transport models. This enables computationally efficient calibration of distance decay, agglomeration effects, and multiple trip purposes. Together with minimization of the Geoffrey E. Havers (GEH) statistic commonly used to evaluate transport models, this bridges a gap between SpNA and mainstream transport modeling practice. The methodology is demonstrated using models of bicycle transport, where the higher resolution of the SpNA models compared to mainstream (four-step) models is of particular use. Additional models are developed incorporating heterogeneous user preferences (cyclist aversion to motor traffic). Based on network shape and flow data alone the best model gives reasonable correlation against cyclist flows on individual links, weighted to optimize GEH (r² = 0.78, GEH = 1.9). As SpNA models use a single step rather than four, and can be based on flow data alone rather than demographics and surveys, the cost of calibration is lower, ensuring suitability for small-scale infrastructure projects as well as large-scale studies.     

A humanitarian logistics model for disaster relief operation considering network failure and standard relief time: A case study on San Francisco district

We propose a multi-depot location-routing model considering network failure, multiple uses of vehicles, and standard relief time. The model determines the locations of local depots and routing for last mile distribution after an earthquake. The model is extended to a two-stage stochastic program with random travel time to ascertain the locations of distribution centers. Small instances have been solved to optimality in GAMS. A variable neighborhood search algorithm is devised to solve the deterministic model. Computational results of our case study show that the unsatisfied demands can be significantly reduced at the cost of higher number of local depots and vehicles.     

Prioritizing low-stress and equitable bicycle networks using neighborhood-based accessibility measures

As cities across the United States strive to create comfortable bicycle networks for mainstream users, three topics have garnered attention: project prioritization criteria, accessibility to everyday destinations, and social equity. However, these topics have not often been integrated in research or practice. This paper introduces a method to assess the extent to which reductions in “Bicycle Level of Traffic Stress” (LTS) on segments of a citywide bicycle network increase accessibility to supermarkets, pharmacies, banks, and public libraries. Six accessibility performance measures are developed and evaluated for 278 neighborhoods in Baltimore, Maryland using a GIS-based approach. The demographic distributions of accessibility results are further analyzed, focusing on disadvantaged populations. Using a set of 106 proposed bicycle projects, the marginal accessibility gains and cumulative demographic impact across affected neighborhoods are assessed for each project. These results are ranked and crosslisted to identify a set of projects that balance accessibility gains with equity objectives. The prioritization results demonstrate some overlap with the short-term priorities embodied in City of Baltimore's 2015 Bike Master Plan, but they also highlight projects in other areas, specifically those that would serve neighborhoods most disadvantaged in terms of racial segregation, high poverty rates, and low rates of vehicle ownership.     

Home-end and activity-end preferences for access to and egress from train stations in the Copenhagen region

Increasing public transport use with the aim of improving the sustainability of cities should focus not only on enhancing level and quality of the service offered, but also on understanding determinants of the choice of access and egress modes to and from the railway network. This study analyzes the difference in preferences at the home-end and activity-end for travelers who have chosen train as their main travel mode while investigating the effect of policy variables such as car parking availability, bicycle parking availability and type, and bicycle on train possibility. Specifically, this study analyzes the choices between five transport modes (i.e., “walk,” “bicycle,” “car driver,” “car passenger,” “bus”) for 2921 home-end and 3658 activity-end trips. Joint mixed logit models are specified and estimated to account for heteroscedasticity and correlation across alternative modes as well as taste heterogeneity across travelers. Model estimates and pseudo-elasticities uncover the importance of travel time and underline how the improvement of walkability, bikeability, and bus service would contribute significantly to the increase in the probability of choosing sustainable modes to and from train stations. Moreover, model results emphasize the role of bicycle parking in terms of the sheer number of spaces to be increased as well as covered places to be offered at the activity end, de facto giving the possibility to leave a bicycle at that end during the night. Lastly, model results show that it is a matter of not only time and trip characteristics, but also traveler characteristics, occupation, and purpose.     

A stochastic programming winner determination model for truckload procurement under shipment uncertainty

We propose a two-stage stochastic integer programming model for the winner determination problem (WDP) in combinatorial auctions to hedge the shipper’s risk under shipment uncertainty. The shipper allows bids on combinations of lanes and solves the WDP to determine which carriers are to be awarded lanes. In addition, many other important comprehensive business side constraints are included in the model. We demonstrate the value of the stochastic solution over one obtained by a deterministic model based on using average shipment volumes. Computational results are given that indicate that moderately sized realistic instances can be solved by commercial branch and bound solvers in reasonable time.     

A GPS data-based analysis of built environment influences on bicyclist route preferences

This study examines the effects of built environment features, including factors of land use and road network, on bicyclists' route preferences using the data from the city of Seattle. The bicycle routes are identified using a GPS dataset collected from a smartphone application named “CycleTracks.” The route choice set is generated using the labeling route approach, and the cost functions of route alternatives are based on principal component analyses. Then, two mixed logit models, focusing on random parameters and alternative-specific coefficients, respectively, are estimated to examine bicyclists' route choice. The major findings of this study are as follows: (1) the bicycle route choice involves the joint consideration of convenience, safety, and leisure; (2) most bicyclists prefer to cycle on shorter, flat, and well-planned bicycle facilities with slow road traffic; (3) some bicyclists prefer routes surrounded by mixed land use; (4) some bicyclists favor routes which are planted with street trees or installed with street lights; and (5) some bicyclists prefer routes along with city features. This analysis provides valuable insights into how well-planned land use and road network can facilitate efficient, safe, and enjoyable bicycling.     

Modelling the joint access mode and railway station choice

This study models the choices of Dutch railway users. We find a steeper negative distance effect on the utility of departure stations accessed by the non-motorized modes of walking and bicycle as compared to the motorized modes of car and public transport. Availability of parking places and bicycle standing areas have a positive effect on the choice of departure railway stations accessed by car and bicycle, respectively. Public transport frequency has a positive whereas travel time has a negative effect on the choice of departure stations accessed by public transport. The derived rail service quality index (RSQI), which provides a measure of rail accessibility to all other stations, has a significant and positive effect on the choice of departure stations accessed by all modes. The outcome of this paper can be used to develop a comprehensive railway accessibility indicator for neighbourhoods, for hedonic pricing studies.     

A scenario planning approach for the flood emergency logistics preparation problem under uncertainty

This paper aims to develop a decision-making tool that can be used by government agencies in planning for flood emergency logistics. In this article, the flood emergency logistics problem with uncertainty is formulated as two stochastic programming models that allow for the determination of a rescue resource distribution system for urban flood disasters. The decision variables include the structure of rescue organizations, locations of rescue resource storehouses, allocations of rescue resources under capacity restrictions, and distributions of rescue resources. By applying the data processing and network analysis functions of the geographic information system, flooding potential maps can estimate the possible locations of rescue demand points and the required amount of rescue equipment. The proposed models are solved using a sample average approximation scheme. Finally, a real example of planning for flood emergency logistics is presented to highlight the significance of the proposed model as well as the efficacy of the proposed solution strategy.     

Location optimisation method for fast-charging stations along national roads

Limited range of electric vehicles is still a huge barrier compared to conventional vehicles. A well-established charging station network, which is derived from users' charging demand, facilitates the spread of electric vehicles and lessens the range anxiety. Several methods have been developed for locating fast-charging stations along national roads in Europe according to the given objective function. In this paper, an arc-based location optimisation method realized by using a geographic information system and greedy algorithm is presented. An ‘oil stain’ deployment strategy is used to achieve even coverage with the minimum number of fast-charging stations along the roads. Several demographic, neighbourhood, and transport-related attributes, as well as the available services that influence the utilization of a fast-charging station, have been identified and their effects have been revealed in a systematic approach. The developed multi-criteria decision-making method has been applied to evaluate the rest areas along motorways and main roads and to propose deployment locations for fast-charging stations. The method was applied for Hungary as a case study and validated using real origin-destination (O-D) data. By the application of the locating method, the user can specify a network character by geographic parameters. The method can be especially beneficial if the O-D flows are unknown. Furthermore, the even distribution of the stations contributes to the high utilization of the fast-charging stations.     

Spatial-interaction network analysis of built environmental influence on daily public transport demand

Many studies have evaluated the influence of the built environment on public transport. Some studies assign subjective weights to environmental factors, which could oversimplify spatial heterogeneity and overlook the temporal dimension. On the other hand, the spatial-interaction network of public transport system is seldom considered. In this paper, we propose an improved framework to explore how individual factors unevenly affect public transport demand over space and time using a geographically and temporally weighted regression (GTWR) model. The proposed framework extends the local built environmental factors by including two network factors extracted from the spatial-interaction network of the public transport system. We conduct a case study in Beijing, China using 686 traffic analysis zones (TAZs). The actual usage of public transport, namely the public transport index (PTI), is estimated by passenger flow divided by the total amount of human flow in a given TAZ. The daily patterns of the spatial heterogeneity in some selected places in the study area is identified and analyzed. It is also found that the estimated coefficient of the variables of the spatial-interaction network is significantly larger than other static environmental factors, indicating that spatial-interaction network can more effectively reflect spatiotemporal heterogeneity in public transport demand. This study provides a better decision-making support for more accurately identifying which factors are most worthy of development, and when and where they can be implemented to improve public transit services.     

Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system

Recent success of bicycle-sharing systems (BSS) have led to their growth around the world. Not surprisingly, there is increased research towards better understanding of the contributing factors for BSS demand. However, these research efforts have neglected to adequately consider spatial and temporal interaction of BSS station's demand (arrivals and departures). It is possible that bicycle arrival and departure rates of one BSS station are potentially inter connected with bicycle flow rates for neighboring stations. It is also plausible that the arrival and departure rates at one time period are influenced by the arrival and departure rates of earlier time periods for that station and neighboring stations. Neglecting the presence of such effects, when they are actually present will result in biased model estimates. The major objective of this study is to accommodate for spatial and temporal effects (observed and unobserved) for modelling bicycle demand employing data from New York City's bicycle-sharing system (CitiBike). Towards this end, spatial error and spatial lag models that accommodate for the influence of spatial and temporal interactions are estimated. The exogenous variables for these models are drawn from BSS infrastructure, transportation network infrastructure, land use, point of interests, and meteorological and temporal attributes. The results provide strong evidence for the presence of spatial and temporal dependency for BSS station's arrival and departure rates. A hold out sample validation exercise further emphasizes the improved accuracy of the models with spatial and temporal interactions.     

Fleet deployment, network design and hub location of liner shipping companies

A mixed integer linear programming formulation is proposed for the simultaneous design of network and fleet deployment of a deep-sea liner service provider. The underlying network design problem is based on a 4-index (5-index by considering capacity type) formulation of the hub location problem which are known for their tightness. The demand is elastic in the sense that the service provider can accept any fraction of the origin–destination demand. We then propose a primal decomposition method to solve instances of the problem to optimality. Numerical results confirm superiority of our approach in comparison with a general-purpose mixed integer programming solver.     

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.     

Spatial patterns and temporal dynamics of urban bicycle crashes—A case study from Salzburg (Austria)

Most bicycle crash analyses are designed as explanatory studies. They aim to identify contributing risk factors and calculate risk rates based on – most of the time – highly aggregated statistical data. In contrast to such explanatory study designs, the presented study follows an exploratory approach, focusing on the absolute number of crashes. The aim is to reveal and describe patterns and dynamics of urban bicycle crashes on various spatial scale levels and temporal resolutions through a multi-stage workflow. Spatial units are delineated in the network space and serve as initial units of aggregation. In order to facilitate comparisons among regions and quantify temporal dynamics, a reference value of crash frequency is simulated for each unit of the respective spatial scale level and temporal resolution.For the presented case study, over 3000 geo-coded bicycle crashes in the city of Salzburg (Austria) were analyzed. The data set covers 10 years and comprises all bicycle crashes reported by the police. Distinct spatial and temporal patterns with clusters, seasonal variations, and regional particularities could be revealed. These insights are indicators for urban dynamics in the transport system and allow for further, targeted in-depth analyses and subsequent counter measures. Moreover, the results prove the applicability of the proposed multi-stage workflow and demonstrate the added value of analyses of small aggregates on various scale levels, down to single crashes, and temporal resolutions.