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 multilevel spatial interaction model of transit flows incorporating spatial and network autocorrelation

While it is well known that ignoring spatial dependence often results in misspecification of models, travel demand models almost never account for this phenomenon. The aim of this paper is to empirically demonstrate the importance of accounting for potential spatial dependence between observations in the specification of spatial interaction models. As a case study, we analyze travel flows on the public transport system in an urban region in the Netherlands. We develop five distinct spatial interaction models (SIMs) of increasing complexity, each encompassing a lower and upper level model. At the lower level, the attractiveness of neighborhoods for boarding and alighting is modeled based on spatial and transit supply characteristics. At the upper level, spatial interactions among zones are modeled taking into account competing origins, competing destinations as well as network characteristics. We systematically compare more traditional SIM formulations with a SIM that explicitly accounts for spatial and network autocorrelation. The results show a substantial difference between the former models and the latter, in terms of the estimated total marginal impacts of the different variables and the pattern of the error terms. The results of our study underscore that the failure to incorporate autocorrelation effects in travel models is likely to influence model outcomes, which in turn may have profound implications for the very design of public transport networks in cities and regions.     

A strategic network choice model for global container flows: specification, estimation and application

Container flows have been booming for decades. Expectations for the 21st century are less certain due to changes in climate and energy policy, increasing congestion and increased mobility of production factors. This paper presents a strategic model for the movement of containers on a global scale in order to analyse possible shifts in future container transport demand and the impacts of transport policies thereon. The model predicts yearly container flows over the world’s shipping routes and passing through 437 container ports around the world, based on trade information to and from all countries, taking into account more than 800 maritime container liner services. The model includes import, export and transhipment flows of containers at ports, as well as hinterland flows. The model was calibrated against observed data and is able to reproduce port throughput statistics rather accurately. The paper also introduces a scenario analysis to understand the impact of future, uncertain developments in container flows on port throughput. The scenarios include the effects of slow steaming, an increase in land based shipping costs and an increased use of large scale infrastructures such as the Trans-Siberian rail line and the opening of Arctic shipping routes. These scenarios provide an indication of the uncertainty on the expected port throughputs, with a particular focus on the port of Rotterdam in the Netherlands.     

A workplace choice model accounting for spatial competition and agglomeration effects

This paper develops a new model of workplace choice for the Sydney Greater Metropolitan Area (SGMA) and describes the way in which this model is integrated into a general modelling framework of MetroScan, an improved version of the Transportation and Environment Strategy Impact Simulator Transportation (TRESIS). The developed model accounts for spatial competition of alternative workplaces via accessibility variables measured to attractions of both the same and different types. The new model also has two new refinements. First, a much finer geographical level is used for modelling worker's choice of workplace given the location of firms and the distribution of jobs. Second, an employment agglomeration effect is incorporated by the inclusion of jobs in the industry class relevant to the worker and two accessibility measures. Modelling analysis on data collected from a survey conducted in Sydney in 2013 identifies highly significant spatial competition and employment agglomeration effects explaining workplace choice. The application of this model to analyse policy relating to the redistribution or growth of jobs within a general framework of MetroScan is discussed.     

A two-stage residential location and transport mode choice model with exposure to traffic-induced air pollution

Air pollution is an increasing concern to urban residents. In response, residents are beginning to adapt their travel behaviour and to consider local air quality when choosing a home. We study implications of such behaviour for the morphology of cities and population exposure to traffic-induced air pollution. To do so, we propose a spatially explicit and integrated residential location and transport mode choice model for a city with traffic-induced air pollution. Intra-urban spatial patterns of population densities, transport mode choices, and resulting population exposure are analysed for urban settings of varying levels of health concern and air pollution information available to residents. Numerical analysis of the feedback between residential location choice and transport mode choice, and between residents' choices and the subsequent potential impact on their own health suggests that increased availability of information on spatially variable traffic-induced health concerns shifts population towards suburban areas with availability of public transport. Thus, health benefits result from reduced population densities close to urban centres in this context. To mitigate population exposure, our work highlights the need for spatially explicit information on peoples' air pollution concerns and, on this basis, spatially differentiated integrated land use and transport measures.     

Continuum modeling approach to the spatial analysis of air quality and housing location choice

Today, air pollution is a great issue, and the transport sector is an important emission source. In this study, we present an integrated land use, transport, and environment model in which transport-related pollutants are assumed to influence people's housing location choices, and a continuum modeling approach is applied. The pollutants generated by the transport sector are dispersed by the wind and they affect air quality. The air quality changes people's housing choices, which in turn changes their travel behavior. We assume that the road users are continuously distributed over the city, that the road network is relatively dense, and that this network can be approximated as a continuum. The total demand is categorized into several classes, and the modeled region contains several subdistricts. People who live in different subdistricts or who belong to different classes of commuters are assumed to have different perceptions of travel time, air quality, and the housing provision–demand relationship. The finite element method and the Newton–Raphson algorithm are adopted to solve this problem, and a numerical valuation is given to illustrate the effectiveness and efficiency of the proposed model.     

Elasticities of mode choice probabilities and market elasticities of demand: Evidence from a simultaneous mode choice/shipment-size freight transport model

This paper presents empirical estimates of market elasticities of demand and elasticities of mode choice probabilities in the intercity freight transport market. Results are derived from a mixed discrete/continuous choice model of mode and shipment size. The mode choice component of the full model was specified as a binary probit function. The two modes considered were rail and regulated common carriers (full truck load). Data was drawn from the US Commodity Transportation Survey consisting of individual shipments of manufactured goods identified at the most disaggregate level. Results obtained in this study are compared with those obtained in previous studies, and areas of similarities and dissimilarities in the magnitude as well as interpretation of the results are highlighted. The own-price and cross-price elasticities of mode choice probabilities were found to vary from 1.44 to 1.88, and from 1.54 to 1.75, respectively. The market price elasticities of demand were found to vary significantly across commodity groups and geographic territories. Among the 40 market segments considered, the truck price elasticity of demand ranged between −0.749 and −2.525; the rail price elasticity of demand was slightly larger, ranging between −0.956 and −2.489; and the rail–truck cross-price elasticity of demand ranged between 0.904 and 2.532.     

Errors in variables in multinomial choice modeling: A simulation study applied to a multinomial logit model of travel mode choice

Modeling travel demand is a vital part of transportation planning and management. Level of service (LOS) attributes representing the performance of transportation system and characteristics of travelers including their households are major factors determining the travel demand. Information on actual choice and characteristics of travelers is obtained from a travel survey at an individual level. Since accurate measurement of LOS attributes such as travel time and cost components for different travel modes at an individual level is critical, they are normally obtained from network models. The network-based LOS attributes introduce measurement errors to individual trips thereby causing errors in variables problem in a disaggregate model of travel demand. This paper investigates the possible structure and magnitude of biases introduced to the coefficients of a multinomial logit model of travel mode choice due to random measurement errors in two variables, namely, access/egress time for public transport and walking and cycling distance to work. A model was set up that satisfies the standard assumptions of a multinomial logit model. This model was estimated on a data set from a travel survey on the assumption of correctly measured variables. Subsequently random measurement errors were introduced and the mean values of the parameters from 200 estimations were presented and compared with the original estimates. The key finding in this paper is that errors in variables result in biased parameter estimates of a multinomial logit model and consequently leading to poor policy decisions if the models having biased parameters are applied in policy and planning purposes. In addition, the paper discusses some potential remedial measures and identifies research topics that deserve a detailed investigation to overcome the problem. The paper therefore significantly contributes to bridge the gap between theory and practice in transport.     

A model for time of day and mode choice using error components logit

The severity of road congestion not only depends on the relation between traffic volumes and network capacity, but also on the distribution of car traffic among different time periods during the day. A new error components logit model for the joint choice of time of day and mode is presented, estimated on stated preference data for car and train travellers in The Netherlands. The results indicate that time of day choice in The Netherlands is sensitive to changes in peak travel time and cost and that policies that increase these peak attributes will lead to peak spreading.     

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.     

Using metro smart card data to model location choice of after-work activities: An application to Shanghai

A location choice model explains how travellers choose their trip destinations especially for those activities which are flexible in space and time. The model is usually estimated using travel survey data; however, little is known about how to use smart card data (SCD) for this purpose in a public transport network. Our study extracted trip information from SCD to model location choice of after-work activities. We newly defined the metrics of travel impedance in this case. Moreover, since socio-demographic information is missing in such anonymous data, we used observable proxy indicators, including commuting distance and the characteristics of one's home and workplace stations, to capture some interpersonal heterogeneity. Such heterogeneity is expected to distinguish the population and better explain the difference of their location choice behaviour. The approach was applied to metro travellers in the city of Shanghai, China. As a result, the model performs well in explaining the choices. Our new metrics of travel impedance to access an after-work activity result in a better model fit than the existing metrics and add additional interpretability to the results. Moreover, the proxy variables distinguishing the population seem to influence the choice behaviour and thus improve the model performance.     

A GIS-based spatial statistical approach to modeling job accessibility by transportation mode: case study of Columbus,Ohio

Improving job accessibility based on transport connectivity helps to address equity issues. Spatial autocorrelation (SA) is also a focus of interest in transportation planning, but has been neglected in analyzing job accessibility in metropolitan areas. In this study, GIS-based job accessibilities by walking, transit, and car are computed for the metropolitan area of Columbus, Ohio, and three transport-based spatial autoregressive (SAR) models are estimated to account for the SA of job accessibility among neighboring block groups, while controlling for built-environment and socioeconomic factors. SA intensities and extents are compared in order to better understand local spatial clusters of job accessibility across the region. Direct and indirect spillover effects due to an investment change in transportation facilities are estimated and provide important transportation planning information. The results also show that walking-accessed jobs are primarily related to physical settings (e.g., land uses) at the local level. Locations with a higher share of zero-vehicle housing units have better job accessibility by transit. There is a spatial mismatch between Asian population clusters and transit-accessed jobs, possibly because of the car-oriented residential clusters around Honda of America Manufacturing in suburban areas. More importantly, locations with a higher share of single-parent households are at a disadvantage in overall job accessibility. Due to its complex transportation needs, a society friendly to single parents should spatially integrate accessible jobs with other needed activities via land-use and transportation planning. Alternatively, car-ownership programs and non-spatial social supports also might be effective to help secure job opportunities and perform daily life activities.     

Using accessibility indicators and GIS to assess spatial spillovers of transport infrastructure investment

This paper proposes a methodology to measure spatial spillovers of transport infrastructure investment and to monetize them by distributing the costs of the infrastructures envisaged according to the regional distribution of the potential accessibility benefits. We use a transport master plan (the Spanish “Plan Estratégico de Infraestructuras y Transporte” 2005–2020, PEIT) as a case study for applying our methodology. In order to calculate and map regional spillovers, economic potential values are computed using network routines in a Geographic Information System (GIS) by comparing two scenarios: firstly, the scenario PEIT 2020; and secondly the scenario which includes the improvements envisaged for the year 2020 in all the regions except the region whose spillover effects are being analyzed. The differences between these two scenarios represent the potential spatial spillover effects of this region on the rest of the regions. This procedure is repeated for each of the Spanish regions in order to calculate a matrix of inter-regional spillovers in economic potential units. In a second step, this matrix is monetized by distributing the costs of the investment in infrastructures envisaged in the region according to the regional distribution of the economic potential benefits. This inter-regional matrix of investments flows characterizes the “inner”, “export”, and “import” values of each of the regional road investments. Subtracting from the direct investment the exports to other regions and adding the imports from other regions, an estimation of the real investment of the plan in each region taking into account all the spillover effects is obtained. This value can be compared with the direct investment in the region, analyzing whether one region has more or less direct investment than real. The proposed methodology makes it transparent which regions benefit more from national transport investment irrespective of where the investment occurs. The spillover matrix can be a valid instrument, especially in federal states or in the case of transnational projects, in the field of regional economics because it offers very useful information for both planners and policy makers.     

Identification of determinants for rescheduling travel mode choice and transportation policies to reduce car use in urban areas

This paper presents a qualitative analysis about the determinants related to rescheduling travel mode decisions during the activity scheduling process. Notably, we were interested to study changes between intention and behavior. Data used came from an in-depth Computer Assisted Telephone Interview (CATI) follow up survey to habitual drivers carried out during the implementation of a panel survey. An interpretative qualitative method based on Analytic Induction was used to cope with the complex nature of rescheduling decisions and the characteristics of the data. The Theory of Planned Behavior has been used to gain a better understanding of the reasons associated with rescheduling travel mode decisions and to obtain a possible explanation of the phenomena studied. In our sample, 12 codes were identified as the main determinants of travel mode changing. Main reasons for rescheduling a travel mode are different considering gender, age, and the type of travel mode change. Main reasons for changing a nonprivate preplanned travel mode to a private travel mode are different considering the type of travel mode preplanned. New determinants of rescheduling decisions different from those associated with other activity scheduling decisions previously identified emerge when analyzing travel mode changes. A number of important sustainable transportation policies to reduce car use in urban areas are derived from the results of this study.     

Evolution trends of the network structure of Spring Airlines in China: A temporal and spatial analysis

Both the sustainable development of China's economy and the deregulation of the China air transport market have acted as a spur for the halting development of low-cost carriers (LCCs) in China. To analyze the development trends of LCCs' network in China, this paper took Spring Airlines, the only LCC in China as an example. First, the winter flight plans of Spring Airlines from 2005 to 2013 were collected. Secondly, the development trends of air transport network of Spring Airlines were explored with methods of mathematical statistics and social network analysis. Additionally, the development trends were analyzed from the levels of navigable cities, air routes and air transport networks. The results show that although Spring Airlines actively launched air routes between tourist cities with non-class I airports, its network has been transformed from a star structure into a complex one with multi-hubs. The development process of the Spring Airlines network can be separated into three stages. In addition, the problems and evolution trends of its network are discussed further.     

The influence of socioeconomic characteristics,land use and travel time considerations on mode choice for medium- and longer-distance trips

This paper contributes to the limited number of investigations into the influence of the spatial configuration of land use and transport systems on mode choice for medium- and longer-distance travel (defined here as home-based trips of 50 km and over) in the Netherlands. We have employed data from the 1998 Netherlands National Travel Survey to address the question as to how socioeconomic factors, land use attributes, and travel time affect mode choice for medium- and longer-distance travel, and how their role varies across trip purposes: commuting, business, and leisure. The empirical analysis indicates that land use attributes and travel time considerations are important in explaining the variation in mode choice for medium- and longer-distance travel when controlling for the socioeconomic characteristics of travellers.     

Investigating household vehicle ownership,mode choice and trip sharing decisions using a combined revealed preference/stated preference Nested Logit model: case study in Bangkok Metropolitan Region

This study proposes a Nested Logit model to investigate household travel behaviour in respect to vehicle ownership, mode choice and trip sharing decisions. The model is analysed using revealed preference (RP) and stated preference (SP) data since a combined estimation of RP/SP data is an effective method of expressing complex travel behaviour and forecasting travel demand for new transport services. In the proposed model, the nesting structure has two levels. The upper level shows car ownership, motorcycle ownership, and no vehicle-ownership choices, and the lower level shows the mode choice combinations for two-traveller households. Trip sharing is considered as one of the mode-choice options in the model. The proposed model is analysed using data from the Bangkok Metropolitan Region. The analysis conducted informs that Central Business District (CBD) travel, long distance travel, household income, job status, age of travellers and presence of school children in households are key aspects in household travel decisions. Based on these aspects, households make important decisions on vehicle ownership, mode choice and trip sharing. In addition, this study reveals commuters’ hidden preferences for modes that are not in existence, in particular the Mass Rapid Transit System in the Bangkok Metropolitan Region due to be fully implemented in 2010.     

A freight transport optimization model for integrated network,service, and policy design

This paper presents a freight transport optimization model that simultaneously incorporates multimodal infrastructure, hub-based service network structures, and the various design objectives of multiple actors. The model has been calibrated and validated using real-life data from the case study of hinterland container transport of the Netherlands, where CO₂ pricing, terminal network configuration, and hub-service networks are chosen as the design measures. Policy packages combining multiple types of policies show better network performance as compared with the optimal performance resulting from a single policy type. This illustrates the value of incorporating multiple types of policies simultaneously in freight transport optimization.     

Dependence between travel distance,individual socioeconomic and health-related characteristics,and the choice of the travel mode: a cross-sectional study for Kaunas,Lithuania

The aim of this study was to analyse the associations between individual socioeconomic and health-related characteristics, travel distance, and the choice of different travel modes in urban population. A cross-sectional study included 932 adults of Kaunas city, Lithuania. The choice of the travel mode and individual characteristics were self-reported by the participants, and their travel routes were calculated using the geographic information system. Multivariate logistic regression was used to assess the most significant factors determining the choice of a car, cycling, walking, or public transport. In total, 529 participants reported using a car, of whom 65.8% had medium or high education levels. These participants were more likely to be younger, male, married, and employed. Among bicycle users, statistically significant differences between the employment status, body mass index, and travel distance were observed. Walkers were significantly more likely to be older, those with lower incomes, unemployed, and travelling the shortest distances. The analysis of the travel distance on the choice of the travel mode revealed that men travelled longer distances with a car compared to women. The employment status was significantly associated with travel distance by car or public transport. Employed individuals travelled longer distances by public transport or by car, compared to unemployed individuals. Among bicycle users, we found that people with higher levels of education and overweight individuals cycled the longest distances. Our study emphasizes the importance of considering different individual characteristics when analysing the choice of transport modes. It provides evidence that is relevant for all urban populations on the choice of the transport mode, particularly considering active versus passive transport.     

Mapping bicycle use and the risk of accidents for commuters who cycle to work in Belgium

This paper explores the spatial patterns of bicycle use for commuting and the risk cyclists run being injured in a road accident when commuting to work in Belgium. Exploratory data analyses suggest that the observed differences in the use of the bicycle to get to work are strongly linked to the urban hierarchy: commuters are more inclined to cycle in cities and specifically in regional towns (with 25 000 to 120 000 inhabitants). In large cities (more than 200 000 inhabitants), less commuting by bicycle takes place. The relationship between bicycle use and the risk of being seriously injured or killed in a road accident is also studied. A cluster analysis confirms that high proportions of commuter cyclists are correlated with low risks of becoming a casualty. It also shows that there are strong spatial differences (regional and between different types of towns) in bicycle use and the risk of an accident. This suggests that cycling policies should be spatially differentiated.