An analysis of drivers route choice behaviour using GPS data and optimal alternatives

This work aims to study drivers' route choices using a dataset of low frequency GPS coordinates to identify travels' trajectories. The sample consists of 89 drivers who performed 42 thousand paths in the province of Reggio Emilia, in Italy, during the seventeen considered months. Four attributes that may be important for the driver are identified and four optimal alternative paths are created based on the selected objectives to evaluate route choice behaviour. The comparison between the characteristics of the paths allows to conclude that drivers select routes that are overall longer than their optimal alternatives but that allow for higher speeds. Furthermore the statistical analysis of drivers' route choices in macroareas evidences that drivers have different behaviours depending on the geography of the territory. Specifically, there is higher heterogeneity of route choices in the plain areas compared to the mountains. In the second part of this work, clusters of repetitive travels are identified and a Geographical Route Directness Index is proposed to identify the areas of the province where the deviation from the shortest alternative path is higher. The analysis shows that, among groups of repetitive travels, the value of the index is higher along the ring road of the city of Reggio Emilia and there is a strong negative correlation between the frequency the driver selects the longer alternative that allow for higher speed, and the number of additional kilometres the same driver has to travel.     

Catalysts and magnets: Built environment and bicycle commuting

What effects do bicycle infrastructure and the built environment have on people’s decisions to commute by bicycle? While many studies have considered this question, commonly employed methodologies fail to address the unique statistical challenge of modeling modes with small mode shares. Additionally, personal characteristics that are not adequately accounted for may lead to overestimation of built environment impacts.This study addresses these two key issues by using an ordered probit Heckman selection model to jointly estimate participation in and frequency of commuting by bicycle, controlling for demographics, residential preferences, and travel attitudes. The findings suggest a strong influence of attitudinal factors, with modest contributions of bicycle accessibility. Bicycle lanes act as “magnets” to attract bicyclists to a neighborhood, rather than being the “catalyst” that encourages non-bikers to shift modes. The results have implications for planners and policymakers attempting to increase bicycling mode share via the strategic infrastructure development.     

Developing advanced route choice models for heavy goods vehicles using GPS data

This paper presents a novel application in route choice modelling using Global Positioning System (GPS) data, focussing on heavy goods vehicles which typically make longer journeys with decisions potentially underpinned by different priorities from those used by car drivers. The scope of the study is larger than many previous ones, using the entire road network of England. Making use of the error components model put forward for route choice by Frejinger and Bierlaire (2007), the work reveals low elasticities in response to changes in travel time, reflecting the limited opportunity for avoiding specific roads on long distance journeys by heavy goods vehicles.     

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.     

Comparison of efficiency between public and private transport modes using excess commuting: An experience in Dar es Salaam

The excess commuting concept is well-known in developed countries. Since the introduction of the concept in the developed countries, the excess commuting framework has been used to derive a set of benchmarks and indices for the analyses of spatial mismatch, jobs-housing balance, and commuting efficiency in urban regions. However, there are very few studies which have examined excess commuting parameters in relation to mode of transport in the current excess commuting literature. Additionally, very few studies on excess commuting have been undertaken in developing countries with no evidence on any study in African cities. This paper attempts to add to the excess commuting literature by examining and comparing excess commuting parameters between public and private transport modes in Dar es Salaam city. The study investigated the effects of land use patterns in the year 2007 (the base year) and the projected land uses in the year 2030 on excess commuting parameters. The results suggest that public transport in Dar es Salaam is very good in terms of providing excellent options to get everywhere in the city as it connects homes and jobs as well as private transport. This is very different from cities in the more developed world, especially in the USA, where public transport is less effective at connecting origins and destinations. It was also found that the land use scenario in the year 2030 encourages a travel pattern that increases the actual average travel distance.     

Allocation of tasks,arrangement of working hours and commuting in different Norwegian households

Weekly working hours and commuting distance can be seen as indicators of equality/inequality between spouses. Traditionally, it is women who adjust their career more readily to meeting family obligations. In an era with a focus on equality between the genders in regard to both education and paid work, it is obvious to think of equality regarding working hours as well, and of distance to and from work. In this study we utilized data from the Norwegian Travel Survey of 2009 to examine the results of adjustments made in weekly working hours and commuting distance in families in which both husband and wife are in paid work. These indicate that the family situation is significant, and that, among other things, children in a family does not lead to any reduction in men’s working hours or commuting distance.Living in the periphery of large cities is disadvantageous for women who want to work full time, while living within a city tends to be to their advantageous in this regard. The results from the analysis of commuting distance show that women do not commute as far as men in comparable groups (working hours, family type, education, place of living, income, access to a car and occupation) and that the policy of regional enlargement is far from gender neutral. So long as it is women who adjust their labour market participation – both temporal and spatial – an enlargement of the regional/geographical labour market resulting potentially in longer commuting distances will primarily favour those who have the possibility to travel irrespectively of family situation, i.e. men, not women.     

An empirical test of modal choice and allocative efficiency: Evidence from US coal transportation

A generalized shipper transportation cost function is estimated to test whether coal shippers achieve allocative efficiency with respect to market prices when facing limited access to the full range of transportation services. Findings indicate that allocative efficiency with respect to market prices is achieved when shippers have access to all major transportation modes. In contrast, the condition for allocative efficiency is not met with respect to market prices when shippers’ modal choices are limited to trucking and rail services. Findings for the sample of shippers who face limited shipping choices is interpreted as suggesting an over-use of trucks relative to the use of trains due to price distortions of transportation services.     

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.     

Day-to-day variation in excess commuting: An exploratory study of Brisbane,Australia

Commuting patterns where most if not all 'trips are optimised relative to a given distribution of jobs and housing can result in personal and socio-economic benefits. Excess commuting indicators provide useful information for academics and policy analysts to evaluate how the actual commuting pattern deviates from an optimal pattern where commuting costs are minimised. While actual commuting patterns vary from day-to-day, academic researchers have yet to quantify the temporal variation in these indicators over short time periods. This may be due to the lack of available longitudinal data as input for excess commuting indicators. This study shows that new, open and/or big data (NOBD) (e.g. smartcard data) can be exploited to serve as the input for such analysis. In this regard, our study uses half a year's worth of smartcard data from Brisbane, Australia to first derive/aggregate origins and destinations by small areas of the probable commuting trips by transit on all 122 weekdays over the study period. The study quantifies the day-to-day variation in excess commuting indicators for these trips and finds that excess commuting indicators vary considerably from one day to the next. Nevertheless, daily variations occur within a relatively consistent range which can be planned for. Our research suggests that more conscious and systematic utilisation of NOBD could change how commuting flows in cities are quantified, monitored and planned. In addition, with NOBD, we can more efficiently detect daily outliers in commuting patterns.     

Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai,China

As the propensity to link multiple intermediate stops in a trip chain (a sequence of journeys that starts and ends at home, includes visiting one or more locations) is more prevalent, the relationship between travel mode choice and trip chain pattern aroused the attention of academics. This paper examines two distinct structures to identify the decision process of travelers between travel mode choice and trip chain pattern: one structure in which trip chain pattern organization precedes travel mode choice, another structure in which travel mode choice decision precedes the organization of trip chain pattern. To accommodate multi-day behavioral variability and unobserved heterogeneity in personal characteristics ignored by traditional travel surveys, multi-day GPS data collected in Shanghai is employed to estimate these two structures within Nested Logit (NL) model. The Monte Carlo (MC) method simulates the switch of trip-chaining and mode choice under possible Transportation Demand Management (TDM) strategies based on estimation results. The findings of this study are as follows: (1) trip chain pattern decision precedes travel mode choice, which means trip-chaining is organized first and affects travel mode choice; (2) complex trip chain is related to higher automobile dependency, and it is a barrier to the tendency to adopt public transit; (3) people who generally travel by automobiles might switch to public transit when private cars are unavailable, and an increase in household bicycle ownership enhances competition between the bicycle and public transit which leads people to turn to cycling. These findings help implement TDM strategies to develop sustainable transportation systems and optimize the urban trip structure.     

Walking,cycling, and public transport for commuting and non-commuting travels across 5 European urban regions: Modal choice correlates and motivations

The objective of this study was to explore individual and contextual-level characteristics associated with active (walking and cycling) and public transport as main travel modes for both non-commuting and commuting purposes, in residents of five European urban regions. We also described participant-reported motivations for modal choice for each journey purpose. The study used multilevel models to investigate cross-sectional associations of individual (i.e. age, gender, educational level) and contextual (defined by a combination of residential neighbourhood characteristics in typologies) characteristics with the choice of active and public transport as outcome. Based on an online survey of 6037 residents of Ghent and suburbs (Belgium), Paris and inner suburbs (France), Budapest and suburbs (Hungary), the Randstad (including the cities of Amsterdam, Rotterdam, The Hague and Utrecht in the Netherlands) and Greater London (United Kingdom), we observed associations with both individual and contextual characteristics.Results of the multilevel modelling show that the probability of using active or public transport as main mode varies depending on both individual and contextual characteristics. At individual level, relations with gender, age, education, weight status and having at least one child varied according to main transport mode and/or purpose. For example, overweight participants reported lower level of cycling for commuting and non-commuting travels than normal-weight participants. In the context of non-commuting travels, participants with one or more child reported less public transport use and more walking (vs participants without children). Among contextual-level variables, urban characteristics of the residential neighbourhood defined by four clusters (according to food environment, recreational facilities and active mobility opportunities) were associated with public transport and walking but not with cycling. For active transport the most important reasons were “I like to travel (on foot or by bike)” and “I want to be physically active” for both travel purposes. “Public transport facilities nearby” was indicated as the most important reason for public transport (for both trip purposes) – the second was “Journey time”.Our findings highlight the importance of exploring a combination of multiple correlates at individual and contextual level according to journey purposes and suggest that the role of health-related individual characteristics such as weight status need further exploration.     

US airport financial reform and its implications for airport efficiency: An exploratory investigation

This study investigates the effect on airport productive efficiency of two major funding sources used by US airports, namely the Airport Improvement Program (AIP) grants and the Passenger Facility Charges (PFC). A two-stage Data Envelopment Analysis (DEA) modeling approach is employed for this purpose. In the first stage, we estimate airport productive efficiency using a variable returns-to-scale DEA model with both desirable and undesirable outputs. In the second stage, random effects regression models are estimated with airport efficiency scores from the first stage as the dependent variable and PFC and a proxy for AIP grants as two of the explanatory variables. By applying the two-stage DEA model to 42 primary US airports, it is found that PFC use has a positive impact on airport productive efficiency, whereas the impact of AIP grants is negative. Multiple counterfactual scenarios are examined by altering the mix of the two types of funding sources. The results show that simultaneously raising the PFC ceiling and decreasing AIP grants could lead to greater airport productive efficiency. The US federal aviation authority would also benefit from realizing these scenarios, especially given the budgetary constraints it faces.     

Travel using managed lanes: An application of a stated choice model for Houston,Texas

Managed lane (ML) travel adds flexibility, but also complexity, to travel choices. Stated choice models (SCMs) are often used for modeling complex transportation choices such as these in an effort to predict demand for these travel options. The design methods for SCMs have evolved from simple orthogonal designs to more sophisticated designs such as D-efficient design that can increase efficiency in estimation. We used three different survey design strategies to produce the stated preference portion of surveys, which were used to elicit travel choices for a sample of Houston travelers. Apart from the D-efficient design we also used random and adaptive random designs to generate attribute levels. There were observable differences in choice behavior depending on what design strategy was used. These differences appear to influence estimates of the value of travel time savings (VTTS) obtained from the random parameter logit (RPL) models estimated using these data. This, in turn, would greatly impact the percentage of travelers predicted to use the MLs.The adaptive random strategy was superior to the other design methods in several categories, and it had similar efficiency to the D-efficient design. However, the mean of VTTS estimate obtained from a D-efficient design was closer to what is typically found in the literature. The difference was considerable and could greatly influence traffic and revenue estimates for the MLs, illustrating the importance of the survey design strategy.     

The social aspect of residential location choice: on the trade-off between proximity to social contacts and commuting

Commuting has been found to be one of the least enjoyable activities. As it is a consequence of the choice of home and work location, the question arises as to how its disutility is compensated. Urban location theory suggests a compensation in the housing or the labor market. While this provides part of the explanation, individuals' personal networks may provide additional insights.Data from a social network survey were used to investigate proximity to social contacts as a factor in residential location choice.The results indicated that proximity to social contacts was an important factor and that it was traded off against commute time. The notion that the disutility of commuting is not compensated for may be a consequence of ignoring the effect of personal networks.The results contribute to the understanding of residential location choice and have implications for urban planning and policies that seek to reduce commuting.     

Built environment and potential job accessibility effects of road pricing: A spatial econometric perspective

This paper analyzes the impacts of the built environment (BE) as it relates to the potential job accessibility (PJA) effects of road pricing. The relationships between the BE elements and PJA under a road charging policy are established using a spatial econometric approach, which uses an integrated land use and transportation model (TRANUS model) and a spatial lag model (SLM). With the intent of further analyzing the differences in the PJA effects of road pricing on traffic analysis zones (TAZs) that contain different combinations of BE elements, a quantitative classification method combining factor and cluster analysis is applied. This will quantitatively categorize TAZs inside and outside the tolled areas. In exploring the relationship between changes in PJA and the road pricing policy, we found the spatial autocorrelation coefficient to be negative. This result suggests that we are unable to increase the PJA of all the regions through road pricing, but rather affect a redistribution of PJA between different regions. Results also indicate that the impacts of road charging on PJA are associated with urban BE elements. Moreover, such effects are the common result of specific characteristics of the BE. The higher the number of jobs, the better the public transportation conditions, and the better the street design (high densities of street and intersections), the less the region will be negatively influenced by a road charging policy, and vice versa. To avoid the negative effects of road pricing on PJA prior to the launch of such a policy, cities should improve public transportation networks and enhance the street design of the road pricing policy areas, especially the toll ring periphery area.     

A study on travelers' transport mode choice behavior using the mixed logit model: A case study of the Seoul-Jeju route

This study is to analyze passengers' choice of the mode of transportation when air transportation is in competition with high speed rail (HSR). The Seoul-Jeju route analyzed as an empirical case study, in which the construction of an undersea tunnel to connect Seoul and Jeju city by HSR has been considered. The study also included two new variables, ‘safety of transportation’ and ‘availability of duty free shopping’ in addition to traditional transport choice variables such as travel time, travel costs and frequency of service to reflect special characteristics of the market. As data gathering tools, SP techniques and mixed logit model, for analytical methodology, were utilized. The authors found that the goodness of fit of the models was improved with new variables. The models also showed that the characteristics of business passengers and leisure passengers in choosing the mode of transportation were different. Business passengers were apt to choose a safety secured mode of transportation regardless of fare while leisure passengers preferred to use duty free shops more than business passengers.     

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.     

Technical efficiency of mainstream airlines and low-cost carriers: New evidence using bootstrap data envelopment analysis truncated regression

Between 2001 and 2005, the US airline industry faced financial turmoil while the European airline industry entered a period of substantive deregulation. Consequently, this opened up opportunities for low-cost carriers to become more competitive in the market. To assess airline performance and identify the sources of efficiency in the immediate aftermath of these events, we employ a bootstrap data envelopment analysis truncated regression approach. The results suggest that at the time the mainstream airlines needed to significantly reorganize and rescale their operations to remain competitive. In the second-stage analysis, the results indicate that private ownership, status as a low-cost carrier, and improvements in weight load contributed to better organizational efficiency.     

Estimating the effect of mobility and food choice on obesity in a rural,northern environment

The literature suggests that energy balance is shaped by where food is accessed, mobility behaviors, beliefs, preferences, socioeconomic characteristics, and attributes of the built environment. However, the relationship of these factors with energy balance has not been simultaneously considered on a regional scale. Utilizing a social-ecological framework, this study incorporated individual level and geospatial data to estimate a structural equation model (SEM) to examine the complex association of these variables with being overweight or obese. Results indicate that food choice, mobility, demographic characteristics, and normative beliefs are related to energy imbalance. Further, accounting for the endogeneity of food access and mobility changes both the significance and magnitude of factors associated with obesity as compared to treating them as exogenous.     

Determinants of route choice behavior: A comparison of shop versus work trips using the Potential Path Area - Gateway (PPAG) algorithm and Path-Size Logit

This paper presents an in-depth comparison of route choice models for work and shop vehicle trips—with emphasis on the interactions between route attributes and individual characteristics—to better understand the route choice determinants that are assumed to vary by trip purpose. Insights into the route choice behavior involving two dominant vehicle trip purposes—work and shop trips—will help in the design of traffic facilities and implementation of measures to influence route choice in the desired direction.In this study, we show that the utility and scale parameters for separate models of work and shop trips differ by direct comparison using a sequential scaling estimation method and likelihood ratio tests, and highlight the differences in route choice behavior by considering the interaction of route attributes and individual characteristics using Path-Size Logit modeling. In the process, we used Potential Path Area - Gateway (PPAG) algorithm—that generates feasible route choice sets for route choice modeling from GPS trajectories of observed routes.The results show that, indeed, route choice behavior varies by trip, which suggests that drivers attach value to route choice determinants relative to trip purpose. The inclusion of interaction terms in model specifications further indicates that work route choice behavior tends to be restrictive compared to the nonrestrictive route choice for shop trips—a generalization consistent with the mandatory and discretionary nature of work and shop trips, respectively. Specifically, individual characteristics such as personal income, age, gender, tenure, household size, and access to public transit affect route choice behavior.