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.     

The spatial impacts model of trans-strait fixed links: A case study of the Pearl River Delta,China

The mage trans-strait fixed links are constructed in different countries to promote regional economic and social development. This paper proposes a spatial impacts model for the trans-strait fixed links in the Pearl River Delta, China. To verify the rationality of the model, four quantitative indicators, including weighted average travel time, the economic linkage intensity, the economic linkage membership grade, and the fractal index are used to investigate the effect by construction of Humen Bridge, Hong Kong–Zhuhai–Macao Bridge and Shenzhen–Zhongshan Bridge. The results show that the Hong Kong–Zhuhai–Macao Bridge and Shenzhen–Zhongshan Bridge greatly improve the regional accessibility with a maximum decrease weighted average travel time of 1.38 h and 0.4 h. The central part of the Delta has greatest impacts. The links increase the economic linkage of cities of each side. The cities directly connected to the fixed links (Hong Kong, Shenzhen, Macao and Zhongshan) experience the highest increasing economic linkage. The regional spatial pattern evolves from unipolarity to multi-polarity and integrates a more advanced and sophisticated spatial network. The theoretical spatial impacts model for the trans-strait fixed links accords with the actual development in the Pearl River Delta.     

Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap

Over the past two decades, smart card data have received increasing interest from transport researchers as a new source of data for travel behaviour investigation. Collected by smart card systems, smart card data surpass traditional travel survey data in providing more comprehensive spatial–temporal information about urban public transport-based (UPT) trips. However, the utility of smart card data has arguably yet to be exploited fully in terms of extracting and exploring the spatial–temporal dynamics of UPT passenger travel behaviour. To advance previous work in this area, this paper demonstrates a multi-step methodology in order to render more insightful spatial–temporal patterns of UPT passenger travel behaviour. Drawing on the Brisbane, Australia, bus network as the case study, a smart card dataset was first processed in combination with General Transit Specification Feed (GTFS) data to reconstruct travel trajectories of bus passengers at bus stop level of spatial granularity. By applying geographical information system-based (GIS) techniques, this dataset was used to create flow-comaps to visualise the aggregate flow patterns at a network level. The flow-comaps uncovered the major pathways of bus passengers and its variations over a one-day period. The differences within the flow-comaps were also quantified to produce weighted flow-comaps that highlighted the major temporal changes of passenger flow patterns along a number of stop-to-stop linkages of the bus network. The proposed methodology visually unveiled the spatial–temporal travel behaviour dynamics of UPT passengers and, in doing so, showed the potential to contribute to a new evidence base with the capacity to inform local public transport policy.     

Toward a spatial understanding of active transportation potential among a university population

Universities and surrounding communities stand to benefit when active travel mode choices are elevated. Despite this, there is little research on travel mode choice at commuter universities and, in particular, the nonlinear spatial relationships among active travel potential and various contextual and compositional factors. The purpose of this study was to examine and visualize linkages among personal, household, density, diversity, and design factors, and active travel (bicycling, walking, and mass-transit modes) among a commuter-university population residing throughout southeastern Michigan, USA. This was accomplished by employing exploratory spatial data analysis (ESDA), ordinary least squares (OLS) regression, and a geographically weighted regression (GWR) model. The GWR model outperformed the traditional OLS model in terms of goodness of fit (R² = .534 and R² = .461, respectively). A novel cartographic mapping technique was employed to depict where statistically significant parameter estimates negatively or positively influenced active travel. The main finding was that personal, household, density, diversity, and design estimates varied in both magnitude and spatiality throughout the university's study area. Interestingly, distance was not a universal barrier to active travel potential. These variations emphasize the importance of promoting active transportation through localized interventions as well as coordinating efforts among universities and surrounding communities.     

The end of travel time matrices: Individual travel times in integrated land use/transport models

To reduce inaccuracies due to insufficient spatial resolution of models, it has been suggested to use smaller raster cells instead of larger zones. Increasing the number of zones, however, increases the size of a matrix to store travel times, called skim tables in transport modeling. Those become difficult to create, to store and to read, while most of the origin-destination pairs are calculated and stored but never used. At the same time, such approaches do not solve inaccuracies due to lack of temporal resolution. This paper analyzes the use of personalized travel times at the finest spatial resolution possible (at x/y coordinates) and a detailed temporal resolution for synthetic agents. The approach is tested in the context of an existing integrated land use/transport model (ILUT) where travel times affect, among others, household relocation decisions. In this paper, person-level individual travel times are compared to traditional skim-based travel times to identify the extent of errors caused by spatial and temporal aggregation and how they affect relocation decisions in the model. It was shown that skim-based travel times fail to capture the spatial and temporal variations of travel times available at a microscopic scale of an agent-based ILUT model. Skims may provide acceptable averages for car travel times if a dense network and small zones are used. Transit travel times, however, suffer from temporal and spatial aggregation of skims. When analyzing travel-time-dependent relocation decisions in the land use model, transit captive households tend to react more sensitively to the transit level of service when individual travel times are used. The findings add to the existing literature a quantification of spatial biases in ILUT models and present a novel approach to overcome them. The presented methodology eliminates the impact of the chosen zone system on model results, and thereby, avoids biases caused by the modifiable spatial unit problem.     

Grocery store accessibility: Different metrics – Different modal disparity results and spatial patterns

Most accessibility studies focus on within transport mode travel performance variations. However, modal accessibility disparity analysis adds value to the single-mode analysis by assessing the interaction between different transport modes and land use. A review of modal disparity studies shows that different accessibility metrics lead to different results, and so it is unclear how this impacts modal accessibility disparity variation. Moreover, the correspondence of the disparity spatial pattern between the different metrics is unclear. This research examines how three typical accessibility metrics (closest facility, cumulative opportunity, space-time constrained) impact modal disparity of grocery store accessibility in Warsaw, Poland. Further, local indicators of spatial association are used to identify areas of similarity and difference between the metrics. This study finds that cumulative opportunities during non-rush hours indicate the best car advantage for all travel times but indicate the best transit advantage during rush hours for 15 min. Generally, the space-time metric indicates better transit accessibility than the closest facility metric which in turn shows better transit accessibility than cumulative opportunities. The city center has significant spatial similarity while peripheral, especially dense, areas have significant spatial difference. Similarity areas have higher transit stop and population densities, while difference areas have average-to-low stop, population, road and store densities.     

The spatial characteristics and influencing factors of modal accessibility gaps: A case study for Guangzhou,China

This paper presents an analysis of the spatial characteristic of the modal accessibility gap (MAG) in Guangzhou based on travel times for public transport and cars to public centers calculated by the Travel O-D point Intelligent Query System (TIQS). Four spatial regression models are used to investigate the effect on the MAG of six factors of the built environment (distance to city public centers, residential density, landuse mix, bus stop density, metro station density and road network density). The results show that travel time for a given origin to destination (OD) trip is likely to be larger by public transport than by car, especially for a trip of longer travel time. The MAG values of all the communities are larger than 0, indicating that when compared with public transport, travelling by car takes less travel time for individuals. Residential density, land-use mix, bus stop density and metro rail station density have significant negative direct impacts and indirect impacts (spatial spillover effects) on MAG, which indicates that appropriate increase in community residential density, providing diversified service facilities within a community, and improving public transport supply are beneficial to promote modal shift. Specifically, the role of metro rail is greater than that of buses. However, the road network density has significant direct positive impacts on MAG, and its spillover effect is also significantly positive, which indicates that building more roads is not an effective way to narrow the accessibility gap between public transport and cars but may facilitate more car travel instead.     

Excess kindergarten travel in Changchun,Northeast China: A measure of residence-kindergarten spatial mismatch

Home-kindergarten travel is an important urban travel activity. This paper firstly discussed the relationship between excess kindergarten travel and residence-kindergarten spatial mismatch. And then questionnaires and GIS are used to provide some baseline analyses of their distribution pattern both statistically and spatially in Changchun. Thirdly, we provide an exploratory analysis of the causes of excess kindergarten travel and spatial mismatch. Results indicate that a) kindergartens in Changchun are highly accessible if children attend their nearest kindergartens, b) however, there is numerous excess travel and high degree of spatial mismatch, nearly 87.1% of the kindergarteners travel excessively, c) the excess travel distances in inner city are much shorter than other areas, and the corresponding spatial mismatch degree is lower. Two critical factors influencing excess kindergarten travel and residence-kindergarten spatial mismatch are summarized. Firstly, the actual causes are a) parents' personal preferences and kindergarten choice tendencies, b) Danwei owned kindergartens generally with higher quality, tendentious enrollment policies, charge threshold, and c) the widespread use of private cars. Secondly, the root causes are limited public expenditure on kindergarten education and its unbalanced allocation, as well as Chinese traditional concept and prevailing way of parenting.     

Exploring the impact of high speed railways on the spatial redistribution of economic activities - Yangtze River Delta urban agglomeration as a case study

The high speed railway (HSR) network in China has developed rapidly over the past ten years, offering a new means of travel and also regenerating and redistributing economic activities by encouraging population mobility. Using the Yangtze River Delta urban agglomeration as a case study, this paper investigates the redistribution of economic activities resulting from HSRs by developing a locally weighted regression model, geographically network weighted regression (GNWR). This GNWR is formulated in light of the current account identity in economics, and incorporates the changes in network-based travel time from HSRs and the degrees of cities, thereby offering a more appropriate method of capturing the movement of economic activities. Importantly, the coefficients in GNWR can reflect the net inflow or outflow of different cities in terms of their residents' investment and/or consumption. The results of the analysis show that HSRs have significantly changed the spatial redistribution of economic activities due to more frequent and rapid mobility. For investment activities, HSR drew the inflow to the second tier cities alongside HSR and strengthened the status of core cities; for consumption activities, HSR motivated the inflow to non-HSR cities located in the peripheral areas.     

The importance of spatial economics for assessing airport competition

This paper explores the relevance of spatial effects on airport competition. Drawing concentric circles of travel distances around it is the most commonly way to define an airport’s catchment area. The characteristics of the catchment area and available substitutes are compared, and assessments of market power made. It is generally recognized that the existence of spatial competition among airports, lies within one market, although sometimes, overlapping circles are examined on the premise that competition lies within these common areas. We look at economic models of spatial competition where there is no overlapping and argue that the stylized facts inferred from economic models of spatial competition have been overlooked. After a short review of airport competition, we introduce economic models for spatial competition and look at the implications of these in the context of airport competition.     

Determinants and spatial variability of after-school travel by teenagers

This research work targeted teenagers to investigate after-school travel patterns and factors affecting their modal choice characteristics in Okinawa, Japan, where teenagers are not allowed to drive. The analysis is based on the cross-sectional data collected for discrete choice modeling of students’ travel in the prefecture. In this study, high school students’ trips from school to home have been analysed using a multinomial logit model. This has revealed the impact of individual, modal, and spatial variables on the mode choice decisions and return trip patterns. A comparative analysis made of downtown and suburb schools exposed differences in travel patterns induced by spatial factors and some existing constraints in modal choice options.     

Examining commuting patterns using Floating Car Data and circular statistics: Exploring the use of new methods and visualizations to study travel times

In this paper, we focus on the spatial distribution of travel time differences between car and public transport during peak and off-peak hours using geospatial methods and circular statistics. To that end, we combine two origin-destination matrices: one contains the number of simulated commuting trips and the other contains accurate travel times measured using either Floating Car Data or transit data. The combination of both matrices reveals that differences in travel time during peak and off-peak hours are high for intra-city commuting and low for inter-city commuting.     

Classification of automobile and transit trips from Smartphone data: Enhancing accuracy using spatial statistics and GIS

As the practices of transportation engineering and planning evolve from “data poor” to “data rich”, methods to automate the translation of data to information become increasingly important. A major field of study is the automatic identification of travel modes from passively collected GPS data. In previous work, the authors have developed a robust modal classification system using an optimized combination of statistical inference techniques. One problem that remains very difficult is the correct identification of transit travel, particularly when the system is operating in mixed traffic. This type of operation generates a wide range of values for many travel parameters (average speed, maximum speed, and acceleration for example) which have similar characteristics to other urban modes. In this paper, we supplement the previous research to improve the identification of transit trips. The method employed evaluates the likelihood that GPS travel data belong to transit by comparing the location and pattern of zero-travel speeds (stopping) to the presence of transit stops and signalized intersections. These comparisons are done in a GIS. The consideration of the spatial attributes of GPS data vastly improves the accuracy of transit travel prediction.     

The impacts of high-speed rail extensions on accessibility and spatial equity changes in South Korea from 2004 to 2018

The construction of South Korean High-Speed Rail (HSR) or Korea Train eXpress (KTX) has been evolving in phases since its first operation in 2004. This development raises concerns whether the benefits from the extended HSR network would again be limited to the initial HSR corridors and will deepen the inequalities in accessibility with the rising issue of uneven regional development of the country. This paper measures the accessibility of each stage of HSR network extension and evaluates its spatial distribution, variation, and changes using weighted averaged travel time and potential accessibility indicators. The results of this study find different accessibility impacts from each stage of HSR extension. Although travel-time reduction and increased attractions have been widened in more cities by each HSR extension, the spatial equity is degenerated by the extension in 2010/2011 as the improvement of accessibility has been concentrated in cities along the primary HSR corridor near the already-advantageous Seoul capital area. In contrast, the future HSR extension in 2018 will enhance equitable accessibility to the isolated regions such as the northeast and the southwest regions of the country. However, the relative degree of accessibility improvement will not be large enough for increasing the spatial equity of accessibility without more extended HSR networks between provinces.     

Generating a spatial coverage plan for the emergency medical service on a regional scale: Empirical versus random forest modelling approach

Adequate spatial coverage by the emergency medical service and the ability to reach any location in the area of interest in the shortest possible time are crucial for the survival of patients with serious conditions. Knowledge of blind spots (i.e. sites that cannot be reached within the required time) represents key information for improving the service quality and may lead, e.g. to a relocation of bases or to other active interventions. Spatial coverage can be derived from experience based on historical data. Such an approach may be problematic if a larger area is being analysed, especially if data is not available for some parts of such areas or if no data is available. To eliminate such problems, we created a prediction model utilising the random forest ensemble learning method. The model is capable of predicting the travel time based on available historical data on ambulance movements (GPS) and the geometric and construction characteristics of individual road segments. We therefore collaborated with the regional public administration and emergency medical service authorities to deliver a time- and resource-efficient solution for emergency spatial planning practice. The outputs from the newly built model were subsequently validated against data from an empirical model currently used by the regional authorities. The results from both models were compared from the perspective of performance in various seasonal and time-of-day conditions. The prediction of travel times using the new model improved according to all the evaluated validation metrics. The importance and applicability of the foregoing model lies in the fact that it can be incorporated into the current emergency medical service management system in a simple manner in terms of data availability and the required computational resources. We conclude that the dynamic model presented in this paper represents an improvement relative to the reference data, and discuss the possibilities of further improving the proposed model.     

Predicting the distribution of households and employment: a seemingly unrelated regression model with two spatial processes

Household and employment counts (by type) are key inputs to models of travel demand and air quality. For a variety of reasons, spatial dependence is very likely present in and across these counts. In order to identify the nature of these unobserved relationships, this study provides the first application of a feasible generalized spatial 3SLS estimation procedure for a seemingly unrelated regression (SUR) model with two spatial processes. Statistical tests reveal that this more generalized model is superior to its constrained versions (e.g., SUR models without spatial components or with just a spatial lag or spatial error process).In the resulting model of Austin, Texas data, local land-use conditions offer substantial predictive power of household and job densities, and transportation access plays a role, as anticipated. The work demonstrates that SUR estimation of land-use intensities from parcel-level data with two types of spatial dependence is feasible and meaningful. Coupled with an upstream model of land-use type, this work offers key inputs for travel demand analyses, with transportation system performance feedback.     

Evaluating spatial accessibility to healthcare services under travel time uncertainty: A reliability-based floating catchment area approach

Most existing healthcare accessibility studies ignore the travel time uncertainty that are commonly encountered in road networks. This study aims to examine the impacts of travel time uncertainty on healthcare accessibility. A reliability-based two-step floating catchment area (2SFCA) method is proposed to evaluate healthcare accessibility under travel time uncertainty. The proposed measure generalizes the conventional 2SFCA measure by explicitly considering individuals' reliability constraints when scheduling visits to healthcare facilities in the face of travel time uncertainty. The proposed measure is further used to investigate travel time uncertainty impacts in a comprehensive case study. A big dataset of taxi trajectories is collected in the case study to extract dynamic information on travel time distributions. The results of the case study highlight the significant but heterogeneous impacts of travel time uncertainty on healthcare accessibility for various parts of the city at different times of the day. They also have several methodological implications for the evaluation of healthcare accessibility under travel time uncertainty.     

Measuring transit use variability with smart-card data

The potential of smart-card data for measuring the variability of urban public transit network use is the focus of this paper. Data collected during 277 consecutive days of travel on a Canadian transit network are processed for this purpose. The organization of data using an object-oriented approach is discussed. Then, measures of spatial and temporal variability of transit use for various types of card are defined and estimated using the data sets presented. Data mining techniques are also used to identify transit use cycles and homogenous days and weeks of travel among card segments and at various times of the year.     

Modeling job accessibility using online map data: An extended two-step floating catchment area method with multiple travel modes

Current quantitative measures of job accessibility rarely consider the interaction between job opportunities and labor force, and the effects of dynamic travel mode choice. Drawing upon multiple open-source datasets, we develop a job accessibility index by extending the two-step floating catchment area method (2SFCA). The job accessibility indices are calculated for different commuting scenarios concerning distance, time, and travel modes. The results suggest that job accessibility is very sensitive to travel modes, and using a single travel mode would contribute to a biased job accessibility index. The job accessibility indices with combined travel modes are more geographically balanced than using a single travel mode. Furthermore, the new index is employed to examine the spatial pattern of job accessibility and explore the relationship between job accessibility, housing, and population in the Pudong district, Shanghai. The new job accessibility indices manifest the impacts of ring roads on the spatial distribution of job accessibility. A comparative analysis shows that the floating population has poor driving-based job accessibility but can access job opportunities using public transit. Also, poor job accessibility leads to low rent prices but has little impact on medium-high rent. Both transit-based and drive-based job accessibility indices are positively related to housing prices. Our study highlights the importance of considering dynamic travel mode choice in job accessibility research. The research outcomes also contribute to the literature on spatial mismatch by revealing the unique relationship between job accessibility, housing, and population in urban China.