Global Positioning System Traces Research Articles

Construction of trail networks based on growing self-organizing maps and public GPS data

Manual creation of trail maps for hikers is time-consuming and can be inaccurate. This paper presents a new method to construct trail networks based on a growing self-organizing map (GSOM) using publicly available Global Positioning System (GPS) data. Unlike other network topology construction techniques, this approach is not dependent on sequential GPS traces. Fine-tuning multiple hyperparameters enables to customize this process based on unique features of datasets and networks. The generated maps, which are trained on public GPS data, are compared to a ground truth from Open Street Map (OSM). The performance evaluation is based on the accuracy, completeness, and topological correctness of the trail maps. The proposed approach outperforms, particularly on sparse networks without significant GPS noise.

Estimating hotspots using a Gaussian mixture model from large-scale taxi GPS trace data

Abstract The data collected from taxi vehicles using the global positioning system (GPS) traces provides abundant temporal-spatial information, as well as information on the activity of drivers. Using taxi vehicles as mobile sensors in road networks to collect traffic information is an important emerging approach in efforts to relieve congestion. In this paper, we present a hybrid model for estimating driving paths using a density-based spatial clustering of applications with noise (DBSCAN) algorithm and a Gaussian mixture model (GMM). The first step in our approach is to extract the locations from pick-up and drop-off records (PDR) in taxi GPS equipment. Second, the locations are classified into different clusters using DBSCAN. Two parameters (density threshold and radius) are optimized using real trace data recorded from 1100 drivers. A GMM is also utilized to estimate a significant number of locations; the parameters of the GMM are optimized using an expectation-maximum (EM) likelihood algorithm. Finally, applications are used to test the effectiveness of the proposed model. In these applications, locations distributed in two regions (a residential district and a railway station) are clustered and estimated automatically.

Spatiality Preservable Factored Poisson Regression for Large-Scale Fine-Grained GPS-Based Population Analysis

With the wide use of smartphones with Global Positioning System (GPS) sensors, the analysis of the population from GPS traces has been actively explored in the last decade. We propose herein a brand new population prediction model to capture the population trends in a fine-grained point of interest (POI) densely distributed over large areas and understand the relationship of each POI in terms of spatiality preservation. We propose a new framework, called Spatiality Preservable Factorized Regression (SPFR), to realize this model. The SPFR is inspired by the success of the recently proposed bilinear Poisson regression and the concept of multi-task learning with factorization approach and the graph proximity regularization. Given that the proposed model is written simply in terms of optimization, we achieve scalability using our model. The results of our empirical evaluation, which used a massive dataset of GPS logs in the Tokyo region over 32 M count logs, show that our model is comparable to the stateof-the-art methods in terms of capturing the population trend across meshes while retaining spatial preservation in finer mesh areas.

Protocol for a cross sectional study of cancer risk, environmental exposures and lifestyle behaviors in a diverse community sample: the Community of Mine study

BackgroundPhysical inactivity and unhealthy diet are modifiable behaviors that lead to several cancers. Biologically, these behaviors are linked to cancer through obesity-related insulin resistance, inflammation, and oxidative stress. Individual strategies to change physical activity and diet are often short lived with limited effects. Interventions are expected to be more successful when guided by multi-level frameworks that include environmental components for supporting lifestyle changes. Understanding the role of environment in the pathways between behavior and cancer can help identify what environmental conditions are needed for individual behavioral change approaches to be successful, and better recognize how environments may be fueling underlying racial and ethnic cancer disparities.MethodsThis cross-sectional study was designed to select participants (n = 602 adults, 40% Hispanic, in San Diego County) from a range of neighborhoods ensuring environmental variability in walkability and food access. Biomarkers measuring cancer risk were measured with fasting blood draw including insulin resistance (fasting plasma insulin and glucose levels), systemic inflammation (levels of CRP), and oxidative stress measured from urine samples. Objective physical activity, sedentary behavior, and sleep were measured by participants wearing a GT3X+ ActiGraph on the hip and wrist. Objective measures of locations were obtained through participants wearing a Qstarz Global Positioning System (GPS) device on the waist. Dietary measures were based on a 24-h food recall collected on two days (weekday and weekend). Environmental exposure will be calculated using static measures around the home and work, and dynamic measures of mobility derived from GPS traces. Associations of environment with physical activity, obesity, diet, and biomarkers will be measured using generalized estimating equation models.DiscussionOur study is the largest study of objectively measured physical activity, dietary behaviors, environmental context/exposure, and cancer-related biomarkers in a Hispanic population. It is the first to perform high quality measures of physical activity, sedentary behavior, sleep, diet and locations in which these behaviors occur in relation to cancer-associated biomarkers including insulin resistance, inflammation, impaired lipid metabolism, and oxidative stress. Results will add to the evidence-base of how behaviors and the built environment interact to influence biomarkers that increase cancer risk.Trial registrationClinicalTrials.gov NCT02094170, 03/21/2014.

Inferring mobility measures from GPS traces with missing data.

With increasing availability of smartphones with Global Positioning System (GPS) capabilities, large-scale studies relating individual-level mobility patterns to a wide variety of patient-centered outcomes, from mood disorders to surgical recovery, are becoming a reality. Similar past studies have been small in scale and have provided wearable GPS devices to subjects. These devices typically collect mobility traces continuously without significant gaps in the data, and consequently the problem of data missingness has been safely ignored. Leveraging subjects' own smartphones makes it possible to scale up and extend the duration of these types of studies, but at the same time introduces a substantial challenge: to preserve a smartphone's battery, GPS can be active only for a small portion of the time, frequently less than $10\%$, leading to a tremendous missing data problem. We introduce a principled statistical approach, based on weighted resampling of the observed data, to impute the missing mobility traces, which we then summarize using different mobility measures. We compare the strengths of our approach to linear interpolation (LI), a popular approach for dealing with missing data, both analytically and through simulation of missingness for empirical data. We conclude that our imputation approach better mirrors human mobility both theoretically and over a sample of GPS mobility traces from 182 individuals in the Geolife data set, where, relative to LI, imputation resulted in a 10-fold reduction in the error averaged across all mobility features.

Predicting Social Anxiety From Global Positioning System Traces of College Students: Feasibility Study.

BackgroundSocial anxiety is highly prevalent among college students. Current methodologies for detecting symptoms are based on client self-report in traditional clinical settings. Self-report is subject to recall bias, while visiting a clinic requires a high level of motivation. Assessment methods that use passively collected data hold promise for detecting social anxiety symptoms and supplementing self-report measures. Continuously collected location data may provide a fine-grained and ecologically valid way to assess social anxiety in situ.ObjectiveThe objective of our study was to examine the feasibility of leveraging noninvasive mobile sensing technology to passively assess college students’ social anxiety levels. Specifically, we explored the different relationships between mobility and social anxiety to build a predictive model that assessed social anxiety from passively generated Global Positioning System (GPS) data.MethodsWe recruited 228 undergraduate participants from a Southeast American university. Social anxiety symptoms were assessed using self-report instruments at a baseline laboratory session. An app installed on participants’ personal mobile phones passively sensed data from the GPS sensor for 2 weeks. The proposed framework supports longitudinal, dynamic tracking of college students to evaluate the relationship between their social anxiety and movement patterns in the college campus environment. We first extracted the following mobility features: (1) cumulative staying time at each different location, (2) the distribution of visits over time, (3) the entropy of locations, and (4) the frequency of transitions between locations. Next, we studied the correlation between these features and participants’ social anxiety scores to enhance the understanding of how students’ social anxiety levels are associated with their mobility. Finally, we used a neural network-based prediction method to predict social anxiety symptoms from the extracted daily mobility features.ResultsSeveral mobility features correlated with social anxiety levels. Location entropy was negatively associated with social anxiety (during weekdays, r=−0.67; and during weekends, r=−0.51). More (vs less) socially anxious students were found to avoid public areas and engage in less leisure activities during evenings and weekends, choosing instead to spend more time at home after school (4 pm-12 am). Our prediction method based on extracted mobility features from GPS trajectories successfully classified participants as high or low socially anxious with an accuracy of 85% and predicted their social anxiety score (on a scale of 0-80) with a root-mean-square error of 7.06.ConclusionsResults indicate that extracting and analyzing mobility features may help to reveal how social anxiety symptoms manifest in the daily lives of college students. Given the ubiquity of mobile phones in our society, understanding how to leverage passively sensed data has strong potential to address the growing needs for mental health monitoring and treatment.

Assessing the consistency of methods to measure travel behavior related to exposure to air pollution

Objective: To compare diary-reported trips and Global Positioning System (GPS) recording trips and identify the reasons for the discrepancies between two methods. Methods: The survey was conducted in Jiangning district in Nanjing city during July to September and November to December, 2015. Both the diary records and GPS method were used to investigate the travel behavior of 33 retired adults for 5 consecutive days. The GPS traces were display in Google Earth and then split into trips to compare with the diary records according to time and location. χ(2) test was used to analyze the influence of trip characteristics on misreporting rates of each method. Results: A total of 1 087 trips in the survey can be compared between the diary (n=909) and the GPS method (n=912). 7.3% (79/1 087) of the trips were only recorded by GPS method, and 11.8% (128/1 087) were only reported in the diary. Of the remaining 880 trips recorded by the both methods, 86.7% (763/880) matched each other, while 13.3% (117/880) did not. For the matched trips, the difference between the trip durations recorded by diary and GPS method was 2.0 (quartile was 6.0) minutes and the diary method overestimated about 25.0% trip durations when compared with the GPS method. The accuracy rates were 84.8%(903/1 065) and 86.9%(925/1 065) for diary and GPS method, respectively. Both methods were more likely to misreport the trip under 5 minutes. The misreporting rates of diary method for trips under 5 minutes were 6.8 times higher than trips over 30 minutes (21.7% vs 3.2%). The reporting accuracy was also significantly different among trips by different travel mode (P<0.05) for both methods, diary method had the lowest accuracy in reporting vehicles recorders (69.3%, 133/192), while GPS method was more accuracy in both biking (91.9%, 136/148) and vehicles recorders(89.6%, 172/192). The main reasons for misreporting by diary method were forgetting or unwilling to record and failing to record trips according to travel mode, while forgetting to carry GPS device, bad GPS signal and failing to locate the position were the main reasons for misreporting by GPS method. Conclusion: The majority of the trips reported in diary and GPS method are well consistent with each other. Both of the methods should be used simultaneously in the survey of time-activity pattern to improve the data quality.

A Performance Evaluation of Location Prediction Position-Based Routing Using Real GPS Traces for VANET

Vehicular ad-hoc network (VANET) is an emerging paradigm for road transportation which minimizes traffic, accidents and improves fuel efficiency. VANET uses the position of the vehicle obtained from satellite system such as global positioning system (GPS), global navigation satellite system, Compass and Galileo as a location id in position-based routing protocol. The position obtained from the satellite system is likely to have an error due to environmental and technical issues which effect the routing performance. Thus, this paper proposes a position-based routing protocol which uses Kalman filter based location prediction technique to improve routing performance by minimizing location error. The routing protocol performance is evaluated on NS-3.23 simulator with real time GPS traces and simulator generated mobility on Two-ray ground and Winner-II propagation model for 500 m transmission range. Further, performance is compared with other prediction-based routing protocol on the metrics of packet delivery ratio, average delay and throughput.

A Method for Extracting Road Boundary Information from Crowdsourcing Vehicle GPS Trajectories.

Crowdsourcing trajectory data is an important approach for accessing and updating road information. In this paper, we present a novel approach for extracting road boundary information from crowdsourcing vehicle traces based on Delaunay triangulation (DT). First, an optimization and interpolation method is proposed to filter abnormal trace segments from raw global positioning system (GPS) traces and interpolate the optimization segments adaptively to ensure there are enough tracking points. Second, constructing the DT and the Voronoi diagram within interpolated tracking lines to calculate road boundary descriptors using the area of Voronoi cell and the length of triangle edge. Then, the road boundary detection model is established integrating the boundary descriptors and trajectory movement features (e.g., direction) by DT. Third, using the boundary detection model to detect road boundary from the DT constructed by trajectory lines, and a regional growing method based on seed polygons is proposed to extract the road boundary. Experiments were conducted using the GPS traces of taxis in Beijing, China, and the results show that the proposed method is suitable for extracting the road boundary from low-frequency GPS traces, multi-type road structures, and different time intervals. Compared with two existing methods, the automatically extracted boundary information was proved to be of higher quality.

CGAM: A community and geography aware mobility model

SummaryThe advances of localization‐enabled technologies have led to huge volumes of large‐scale human mobility data collected from Call Data Records (CDR), Global Positioning System (GPS) tracking systems, and Location Based Social networks (LBSN). These location data that encompass mobility patterns could generate an important value for building accurate and realistic mobility models and hence important value for fields of application including context‐aware advertising, city‐wide sensing applications, urban planning, and more. In this paper, we investigate the underlying spatio‐temporal and structural properties for human mobility patterns, and propose the Community and Geography Aware Mobility (CGAM) model, which characterizes user mobility knowledge through several properties such as home location distribution, community members' distribution, and radius of gyration. We validate the CGAM synthetic traces against real‐world GPS traces and against the traces generated by the baseline mobility model SMOOTH and assess that CGAM is accurate in predicting the performance of flooding‐based and community‐based routing protocols.

Mode choice with latent availability and consideration: Theory and a case study

Over the last two decades, passively collected data sources, like Global Positioning System (GPS) traces from data loggers and smartphones, have emerged as a very promising source for understanding travel behaviour. Most choice model applications in this context have made use of data collected specifically for choice modelling, which often has high costs associated with it. On the other hand, many other data sources exist in which respondents’ movements are tracked. These data sources have thus far been underexploited for choice modelling. Indeed, although some information on the chosen mode and basic socio-demographic data is collected in such surveys, they (as well as in fact also some purpose collected surveys) lack information on mode availability and consideration. This paper addresses the data challenges by estimating a mode choice model with probabilistic availability and consideration, using a secondary dataset consisting of ‘annotated’ GPS traces. Stated mode availability by part of the sample enabled the specification of an availability component, while the panel nature of the data and explicit incorporation of spatial and environmental factors enabled estimation of latent trip specific consideration sets. The research thus addresses an important behavioural issue (explicit modelling of availability and choice set) in addition to enriching the data for choice modelling purposes. The model produces reasonable results, including meaningful value of travel time (VTT) measures. Our findings further suggest that a better understanding of mode choices can be obtained by looking jointly at availability, consideration and choice.

On the role of bridges as anchor points in route choice modeling

This work builds upon the thought that individuals allocate higher levels of importance to some particular features of the route, so called anchor points. Previous route choice models have either ignored the effects of anchor points (route-based models), or have given an exclusive attention to their effects and ignored the behavioral accuracy and practicality of these models (anchor-based models). In this work we argue that the consideration of both route-level attributes and anchor points would enhance the behavioral aspect of route choice models as well as their estimation and prediction abilities. Global Positioning System traces have been used to investigate the effect of bridges as anchor points for trips between Montreal and its Northern suburb, Laval. A classic Nested Logit and a nested Logit Kernel model have been estimated, in which interdependencies among routes crossing the same bridge are captured through the nested structure and the adopted factor analytic approach, respectively. A Metropolis–Hastings path-sampling algorithm is applied, for the first time, on a large road network with more than 40,000 nodes and 19,000 links to provide the consideration choice set. Estimates are then compared to three alternate models, representing route-based and anchor-based formulations; namely Path-Size Logit, Extended Path-Size Logit, and Independent Availability Logit models. Empirical results showed that the proposed nested structures with MH sampling provide better estimates and also perform better in the validation step with respect to comparative models. Findings underscore the importance of considering anchor points in conjunction with route level attributes in route choice decisions.

Map-Matching Poor-Quality GPS Data in Urban Environments: The pgMapMatch Package

Global Positioning System (GPS) data have become ubiquitous in many areas of transportation planning and academic research. The usefulness of GPS data often depends on the points being matched to the underlying street network – a process known as “map matching.” Formally, the map-matching problem is to identify the true path, consisting of a sequence of street edges (i.e. blocks, or segments between intersections), given a sequence of GPS coordinates. Several algorithms have been developed to map-match GPS data. However, there are three major shortcomings of existing approaches. First, some algorithms assume that the user takes the fastest route, and does not circle or deviate. This is an inappropriate assumption in contexts where drivers circle in search of a parking space or have a personal preference for a circuitous route. Second, published algorithms often leave the implementation to the user, and do not provide code or a software implementation. Third, when map-matching large datasets where the matches cannot be individually inspected, published algorithms do not provide a way to evaluate the likelihood that the match is correct. This paper presents a new map-matching algorithm that is designed for use with poor-quality, heterogeneous GPS traces in urban environments, where drivers may circle for parking and GPS quality may be affected by underground parking, tall buildings and similar obstacles. The paper is accompanied by open-source Python code that is designed to work with PostGIS spatial databases. In a test dataset that includes many poor-quality traces, our new algorithm accurately matched 87% of traces, compared to 67% in a widely available alternative.

A cloud‐based taxi trace mining framework for smart city

SummaryAs a well‐known field of big data applications, smart city takes advantage of massive data analysis to achieve efficient management and sustainable development in the current worldwide urbanization process. An important problem in smart city is how to discover frequent trajectory sequence pattern and cluster trajectory. To solve this problem, this paper proposes a cloud‐based taxi trajectory pattern mining and trajectory clustering framework for smart city. Our work mainly includes (1) preprocessing raw Global Positioning System trace by calling the Baidu API Geocoding; (2) proposing a distributed trajectory pattern mining (DTPM) algorithm based on Spark; and (3) proposing a distributed trajectory clustering (DTC) algorithm based on Spark. The proposed DTPM algorithm and DTC algorithm can overcome the high input/output overhead and communication overhead by adopting in‐memory computation. In addition, the proposed DTPM algorithm can avoid generating redundant local trajectory patterns to significantly improve the overall performance. The proposed DTC algorithm can enhance the performance of trajectory similarity computation by transforming the trajectory similarity calculation into AND and OR operators. Experimental results indicate that DTPM algorithm and DTC algorithm can significantly improve the overall performance and scalability of trajectory pattern mining and trajectory clustering on massive taxi trace data. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Map‐matching algorithm applied to bicycle global positioning system traces in Bologna

A novel map-matching algorithm is proposed, implemented and applied to global positioning system (GPS) traces which have been recorded by cyclists in Bologna, a medium-sized city in the North of Italy. The algorithm has been developed to match geo-referenced traces to a sequence of edges of a given road network model. Map-matching for bike trips is particularly challenging as cyclists often use footpath or parks which are not necessarily represented by the road network model. The matching algorithm should smartly tolerate the lack of network information. The algorithm should also be fast and capable of processing thousands of GPS traces in a reasonable time. The proposed probability-based method, which also exploits information on various network attributes, allows a reliable and fast map matching, even in dense street networks and with interrupted GPS data streams. In fact, one serious issue is to find a reliability measure which allows to verify the matched routes, without the knowledge of the real routes, as the available cyclist traces are anonymous. In addition to the reliability check, a sensitivity analyses with respect to the most relevant parameters has been conducted. The advantages of the proposed map-matching algorithm are quantified through a direct comparison with a topology-based map-matching algorithm from literature.

A simulator for opportunistic networks

SummaryWhen mobile devices involved in a communication process are unable to establish a direct connection, or when communication should be offloaded to cope with large throughputs, mobile collaboration can be used to enable communication through opportunistic networks. These types of networks are formed when mobile devices communicate only using short‐range transmission protocols, usually when users are close. Routes are built dynamically, because each mobile device is acting according to the store‐carry‐and‐forward paradigm. Thus, contacts are seen as opportunities to move data towards the destination. In such networks, the routing protocol is of vital importance, and today, we witness quite a number of routing algorithms that have been proposed to maximize the success rate of message delivery whilst minimizing the communication cost. Such protocols take advantage of the devices history of contacts, or information about users carrying the mobile devices, to make their forwarding decision. This paper extends our previous work with the following: First, we describe a new simplified, fast simulator, designed to minimize the work needed to conduct extensive tests for opportunistic routing algorithm on multiple traces; next, we analyze extensively several of the most popular routing algorithms through extensive simulations conducted using our simulation platform. We highlight their pros and cons in different scenarios, considering different real‐world mobility data traces, such as Global Positioning System traces. The raw Global Positioning System traces are converted to a format based on encounters between participating entities. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Travel time estimation at intersections based on low-frequency spatial-temporal GPS trajectory big data

ABSTRACTIntersections are the critical parts where different traffic flows converge and change directions, forming “bottlenecks” and “clog points” in urban traffic. Intersection travel time is an important parameter for public route planning, traffic management, and engineering optimization. Based on low-frequency spatial-temporal Global Positioning System (GPS) trace data, this article presents a novel method for estimating intersection travel time. The proposed method first analyzes the different travel patterns of vehicles through an intersection, then determines the range of an intersection dynamically and reasonably, and obtains traffic flow speed and delay at the intersection under different travel patterns using a fuzzy fitting approach. Finally, the average intersection travel time is estimated from traffic flow speed and delay and intersection range in different travel patterns. Wuhan road network data and GPS trace data from taxicabs were tested in the experiments and the results show that the proposed method can improve the accuracy of travel time estimation at city intersections.

Using GPS-enabled mobile phones to characterize individuals’ activity patterns for epidemiology applications

GIScience 2016 Short Paper Proceedings Using GPS-enabled mobile phones to characterize individuals’ activity patterns for epidemiology applications E.-H. Yoo and Y.-S. Eum University of Buffalo, SUNY, Buffalo, NY, USA Email: {eunhye, yeum}@buffalo.edu Abstract We assessed the potential of global positioning system (GPS)-equipped mobile phones for health-related studies. We demonstrated the use of GPS data as a means of collecting individuals’ activity patterns for personal exposure assessments and public health surveillance. The widespread use of mobile phones has enabled investigators to conduct exposure studies and to detect infectious disease at the individual level on a massive scale. However, still substantial uncertainties are present in converting raw GPS data into relevant information. To address these issues, we proposed three algorithms for pre-processing and classification of raw GPS data, and demonstrated their applications to real world data in a case study. 1. Introduction Exposure models typically impose unrealistic assumptions such that subjects within a neighborhood are equally exposed to air pollution and/or most individuals spend their time at their residences. Similarly, a lack of understanding of human movement, which is an important component of disease transmission, has been considered as an obstacle to develop effective national communicable disease control programs. In exposure modelling, some improvements have been achieved by adopting a microenvironment (ME) approach where individuals’ time spent at MEs, such as outdoors, residence, and workplace, was explicitly taken into account. However, collecting the information on individuals’ time-activity patterns has been cost-, time-, and labor-intensive with limited reliability and accuracy. Comparably, aggregated data have limited efforts to reconstruct the complex and dynamic nature of real- world contact networks, which plays a critical role of contact network in an outbreak of dangerous infectious disease. The emergence of lightweight, low-cost, and accurate GPS devices has provided a promising tool for objectively assessing the geographic positions of the environmental context in which health-related behaviors take place (Schipperijn et al. 2014). GPS technology enabled investigators to capture daily trajectories of individuals with higher temporal resolution at increasing locational precision (Gerharz et al. 2013, Dias & Tchepel 2014), although the use of GPS data in health research is not without challenges. As reviewed by Krenn et al. (2011), the positional accuracy of GPS data collected in real world is often unacceptable in health studies, especially, over longer study periods. The data quality of GPS traces depends on the amount of data lost from signal drop-outs, loss of device battery power, and poor adherence of participants to following the specific research protocol. Despite the advancement in GPS technology, signal acquisition is still affected by the presence of tall buildings and significant uncertainties associated with the processing and classifying raw data are present. In this study we focus on the GPS data collected from a mobile phone with and without data connection. Our primary goal is to identify major MEs associated with health-related activities using GPS data. First we developed and applied a “selective resampler” to raw GPS data for pre-processing. Using the processed data, we identify significant places and travel modes using the two automated classification schemes.

A novel approach for generating routable road maps from vehicle GPS traces

Public vehicles and personal navigation assistants have become increasingly equipped with single-frequency global positioning system (GPS) receivers or loggers. These commonly used terminals offer an inexpensive way for acquiring large volumes of GPS traces, which contain information pertaining to road position and traffic rules. Using this new type of spatial data resource, we propose a novel approach for generating high-quality routable road maps. In this approach, a simplified road network graph model uses circular boundaries to separate all GPS traces into road intersections and road segments and builds road networks that maintain their identical geometric topologies through the entry/exit points at the original boundaries. One difficulty inherent to this type of approach is how to best determine the appropriate spatial coverage for road intersections. Conflict points among GPS traces that have large intersection angles usually occur within the physical areas of road intersections, particularly those involving left turns. Therefore, we determined a proper circle boundary for individual road intersections by conducting a spatial analysis of such feature points. This approach was implemented using Python and PostgreSQL/PostGIS and was tested in Huaibei City, China. Based on a comparison with human-interpreted results, the automatically generated routable road map was demonstrated to be of high quality and displayed detailed road networks with turning at various at-grade intersections, interchanges and U-turns.

Unraveling the Travel Behavior of Carsharing Members from Global Positioning System Traces

As carsharing becomes increasingly popular throughout the world, it would be of interest to understand better the underlying characteristics of the trips made by members when they use the cars. To date, few studies have reported carsharing trip details. This paper presents a methodology to analyze three components of a station-based carsharing member's journey: the locations of the stops, the attributes of the trips, and the characteristics of the trip chains. The method is based on the processing of Global Positioning System traces collected onboard car-sharing vehicles; a 5-min stop criterion is used to divide the trip chains into separate trips. The case study involves the Communauto system in the Greater Montreal area, Quebec, Canada. The study shows that carsharing members make more trips within their trip chains than typical car owners do. However, those trips are shorter and are often made for purposes other than work (shopping or visiting, for example). Members tend to maximize the use of the cars during the rental period (the members are on the move up to 50% of the time for short trip chains and 30% of the time for longer trips).