Resident Travel Analysis based on GPS Trajectory

Commuting carbon emissions are an essential component of urban carbon emissions, and determining how to reduce them is an area of great debate among researchers. The current research lacks a tool and instrument that can extensively account for residents’ commuting. Traditional methods are mainly based on questionnaire surveys, which have low accuracy at spatial and temporal aspects. High accuracy carbon emission accounting methods can effectively assist urban planning and achieve precise urban emissions reductions. This study applies a taxi commuting carbon emissions accounting method divided into two main steps. Firstly, the carbon emissions of taxi trajectories are calculated using taxi trajectory data and a carbon emission calculation method developed based on VSP. Secondly, the taxi trajectory and POI data are used to filter the commuter trajectory with the help of a two-step moving search method. In this way, the taxi commuting carbon emissions were obtained. Then, the spatial distribution characteristics of residential taxi commuting carbon emissions are analysed by spatial autocorrelation tools, which could facilitate low carbon zoning management. A typical working day in Hangzhou was selected as the research object of this study. The results show that (1) morning peak commuting carbon emissions in the main urban area of Hangzhou reached 2065.14 kg per hour, accounting for 13.73% of all taxi travel carbon emissions; and evening peak commuting carbon emissions reached 732.2 kg per hour, accounting for 4% of all taxi travel carbon emissions; (2) At the grid level, the spatial distribution of commuting carbon emissions in Hangzhou shows a single central peak that decays in all directions; and (3) The results at the resident community scale show that urban public transport facilities influence resident community commuting carbon emissions. In areas such as at the urban-rural border, resident community commuting carbon emissions show high levels of aggregation, and in the main urban area, resident community commuting carbon emissions show low levels of aggregation. This study not only provides a new method of commuting investigation but also offers constructive suggestions for future carbon emission reduction under Hangzhou’s urban planning.

Analyzing and mining geo-referenced trajectory data has different aspects to researchers from different communities. For example, animal location data provides ecologists live points of contact between ecologies and the species. And by studying movements of individual animals, they have gained insight into population distributions, important resources, dispersal settings, social interaction or general patterns of how the space was used in an ecological system. Similarly, geologists and environmentalists use earthquake positional data for predicting the location of the next earthquake. Intelligent Transportation Systems and GIS communities use heuristic algorithms on vehicle trajectory data sets to construct or update digital street-maps that represent the data set. Recently, the Computational Geometry community started to give attention to the street-map construction problems as well, applying different approaches and providing quality guarantees. Although different communities use different types or aspects of the GPS data, they face one challenge in common: how to model or incorporate the impreciseness of the input data in their output. In this paper we discuss specifically the impact of spatial inaccuracy of GPS trajectory data on street-map reconstruction algorithms. In particular, we discuss approaches and challenges to associate that impreciseness with the reconstructed street-intersections.

Taxi GPS trajectories contain massive spatial and temporal information of urban functions and residents' activities. The mining of hotspots and moving patterns of residents from taxi trajectory data is of great significance for urban planning, traffic management, public travel services, etc. Because of the existing hotspots region mining methods have low computational efficiency in the face of large-scale trajectories data, we build a directed weighted network to model intra-city spatial interactions and use the DWNodeRank algorithm to reveal hotspots. Firstly, the urban area is divided into grid cells, and the origin-destination matrix is produced by travel records between grid cells. Secondly, a directed weighted network is built based on origin-destination matrix. Finally, the taxi trajectory data of Lanzhou city in China are taken as the experimental data. From the result, we find the distribution and strength of hotspots area in Lanzhou city.

Taxi flow is an important part of the urban intelligent transportation system. The accurate prediction of taxi flow provides an attractive way to find the potential traffic hotspots in the city, which helps to avoid serious traffic congestions by taking effective measures in advance. The current prediction of taxi flow and its impact on urban transportation are closely related to the passenger origin-destination (OD) information. However, high-quality OD information is not always available. To address this problem, a prediction model, named as TaxiInt, is proposed in this study. Different from other density-clustering-based approaches, neural network, or OD information based models, TaxiInt predicted the taxi flow using the trajectory data of taxis. The spatial features and temporal features of each road were extracted using a graph convolutional network, which was trained with the road network information and the trajectory data. The experiments carried on a real taxi dataset showed the validity of our model. It can predict the taxi flow at a given urban intersection with high accuracy.

Advances in positioning and wireless communicating technologies make it possible to collect large volumes of trajectory data of moving vehicles in a fast and convenient fashion. These data can be applied to traffic studies. Behind this application, a methodological issue that still requires particular attention is the way these data should be spatially visualized. Trajectory data physically consists of a large number of positioning points. With the dramatic increase of data volume, it becomes a challenge to display and explore these data. Existing commercial software often employs vector-based indexing structures to facilitate the display of a large volume of points, but their performance downgrades quickly when the number of points is very large, for example, tens of millions. In this paper, a pyramid-based approach is proposed. A pyramid method initially is invented to facilitate the display of raster images through the tradeoff between storage space and display time. A pyramid is a set of images at different levels with different resolutions. In this paper, we convert vector-based point data into raster data, and build a gridbased indexing structure in a 2D plane. Then, an image pyramid is built. Moreover, at the same level of a pyramid, image is segmented into mosaics with respect to the requirements of data storage and management. Algorithms or procedures on grid-based indexing structure, image pyramid, image segmentation, and visualization operations are given in this paper. A case study with taxi trajectory data in Shanghai is conducted. Results demonstrate that the proposed method outperforms the existing commercial software.

Urban density not only represents the intensity of travel demand, but also serves as a useful tool for quantifying the elements that influence travel behavior and demand. In this study, Khulna City of Bangladesh has been selected as a case study to explore the impacts of urban density on travel behavior in a city of a developing country. The average characteristics of urban density, as well as their impacts on travel behavior and demand, have been studied using geospatial analysis techniques. Urban densities have been categorized into Single Density (Population Density, Employment Density, and Building Density) and Composite Density (Household to Work trip Density, Household to School trip Density and Household to Shop trip Density) based on characteristics of travel behavior and demand. Descriptive statistics have been used to explore the impacts of urban densities on the various trip characteristics such as trip production, trip distance, trip cost, trip time, and trip mode. Diversity of trips and overall trip attraction areas have been identified using the Shannon diversity index and other spatial analyses. Trip production, trip distance, trip time, trip cost, and mode choice all show a positive linear relationship with urban single-density, according to this study. The everyday activities of residents are closely related to urban composite densities, and trip production increase in tandem with composite densities. It has been concluded that different urban densities have significant impacts on travel demand and travel behavior.

Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel

In urban road traffic system, a variety of travel modes coexist and interact with each other. Micro modeling and analysis methods are difficult to capture the macro evolution characteristics and interaction of traffic flow. Based on the actual road network and vehicle trajectory data of Beijing, this paper calculates the total number of vehicles on the road section through the taxi trajectory data and the safe distance model to obtain the social vehicle data, and uses the bus moving trajectory data to obtain the bus vehicle data. Based on the three-dimensional macroscopic fundamental diagram (MFD) method, a three- dimensional MFD of social vehicle-bus vehicle is established, and the model parameters are solved by the least square method. The traffic flow data obtained by processing is used to verify its existence. According to the results of regional division, the influence of the number of buses in the two regions on the traffic capacity in the region is compared. The three-dimensional MFD model constructed in this paper can reflect the evolution characteristics of actual traffic flow, which is of great significance for traffic control of different trips.

ABSTRACTDetailed real-time road data are an important prerequisite for navigation and intelligent transportation systems. As accident-prone areas, road intersections play a critical role in route guidance and traffic management. Ubiquitous trajectory data have led to a recent surge in road map reconstruction. However, it is still challenging to automatically generate detailed structural models for road intersections, especially from low-frequency trajectory data. We propose a novel three-step approach to extract the structural and semantic information of road intersections from low-frequency trajectories. The spatial coverage of road intersections is first detected based on hotspot analysis and triangulation-based point clustering. Next, an improved hierarchical trajectory clustering algorithm is designed to adaptively extract the turning modes and traffic rules of road intersections. Finally, structural models are generated via K-segment fitting and common subsequence merging. Experimental results demonstrate that the proposed method can efficiently handle low-frequency, unstable trajectory data and accurately extract the structural and semantic features of road intersections. Therefore, the proposed method provides a promising solution for enriching and updating routable road data.

Social activities are responsible for an important portion of trips and they constitute the fastest growing segment of travel. Moreover, social activities and mobility are important aspects of people’s quality of life. Therefore, social activities are important for transport planners to take into account. As social activities involve meeting with other persons at a certain time and location, the spatial-choice behavior for social activities is critical for successful urban design and planning. Over the last decades, researchers have attempted to explain individual (social) travel behavior as a result of socio-demographic and land-use variables. However, recently, it has been acknowledged that other variables that offer opportunities for social mobility have to be added. As social travel demand is derived from a wish or need to perform activities with other people, it is important to incorporate social networks into the study. Moreover, social activities are likely to be influenced by the use of new information and communication technologies (ICT’s), as these ICT’s offer new possibilities for the maintenance of social networks that are becoming more geographically spread. Knowledge about the relationship between ICT’s, social networks and social activity-travel behavior is rather limited. In order to assess future transportation needs for social activities, research is needed. The aim of this dissertation is therefore to study the way in which social activity-travel patterns are influenced by people’s personal characteristics, properties of the built environment, ICT-use and social networks. To that end, a data collection instrument was designed. The instrument consists of a two-day paper-and-pencil social interaction diary a questionnaire on personal characteristics and a follow-up questionnaire on personal social network members. The social interaction diary captured data on ICT-mediated social interactions, as well as face-to-face social activities and travel for these activities. In addition, the social interaction diary was used to collect data on the people with whom the respondents interacted during those two days, including age, gender, social category, distance and frequency of communication with different modes. In addition to the social interaction diary a questionnaire was designed to gather data on the respondents’ personal and residential characteristics. To allow the estimation of the impact of the built environment a requirement for the data collection was to secure sufficient variation in characteristics of the built environment. To this end, the sample was stratified by urban density. The data were collected between January and March 2008 in the region of Eindhoven, among 747 respondents, aged 15 or over. To capture the respondents’ social networks, a follow-up questionnaire was used, which was mailed to a subsampleof the respondents. In this social network questionnaire, respondents were asked to record information on all their social network members, including gender, age, social category, how long they have known each other, distance to their homes and the frequency of interaction with each alter by different modes (face-to-face, telephone, SMS, e-mail, IM). The social network questionnaire was completed by 116 respondents. Quantitative analyses were performed on the data. The analyses focus on exploring and testing multivariate relationships in the data. Analyses were carried out to examine the effects of personal characteristics and properties of the built environment on size and composition of social networks; contact frequency and choice between different communication modes, and social activity-travel patterns. The analyses also explored to what extent and how the nature and strength of the relationship between personal characteristics and properties of the built environment and social activity-travel patterns is mediated by ICT-mediated communication and characteristics of social networks. Regression and discrete choice models were used to predict one dependent variable as a function of one or more independent variables. To analyze simultaneously the direct and indirect dependencies between use of communication technology, social networks and activity-travel patterns, a structural equation approach was used. Because the data have a hierarchical structure (individuals (egos) have multiple alters or interactions), multi-level analysis was used. The estimation results of the models allow us to reconstruct the generation of social activities and travel demand for social purposes. The empirical results suggest that the built environment, ICT-use and social networks all play an important role in the generation of social activity-travel and have to be taken into account in transport policy.

Aiming to demand for large-scale trajectory data visualization, this paper proposes a visualization-oriented trajectory data compression method to solve the problem that the existing trajectory data compression methods excessively pursue the optimal trajectory data compression rate, which makes it difficult to fully consider the shape similarity between the compressed trajectory and the original trajectory. The method comprehensively considers the time, position, direction and other information of the trajectory data to judge the key feature points of the trajectory data. And the method can compress the trajectory data by setting the compression ratio in advance according to the line of sight and the visual range, which can better meet the visual requirements. During the compression process, the shape of the trajectory is better controlled, which makes it possible to enhance the visualization effect of the trajectory data while achieving efficient compression of large-scale trajectory data.

A customized bus (CB) system is a new emerging public transportation that provides flexible demand-oriented transit services for city commuters. Existing CB systems encounter two challenges of 1) collecting travel demands and discovering travel patterns effectively and efficiently and 2) planning profitable bus lines based on travel patterns. In this paper, we propose a bus line planning framework, called T2CBS, by taking full advantage of taxi trajectory data. In T2CBS, similar travel demands are discovered from passenger trajectories with a clustering algorithm, and CB stops are deployed at pick-up and drop-off points of trajectory clusters with integer linear programming. To plan profitable CB lines, we propose a profit estimation model, by considering the number of taxi passengers who can be attracted to CB buses. A routing algorithm (CBRouting) and a timetabling algorithm (CBTimetabling) are proposed to generate a CB line that can achieve the maximum profit for each trajectory cluster. We conduct experiments on one-month taxi trajectory data in Nanjing, China. Experimental results demonstrate that our T2CBS can generate CB lines with higher profit compared with baseline methods, and the moderate increase in travel time along the CB lines is significantly dominated by the savings in bus fare.

Raw GPS trajectory data are often very large and use up excessive storage space. The efficiency and accuracy of activity patterns analysis or individual–environment interaction modeling using such data may be compromised due to data size and computational needs. Line generalization algorithms may be used to simplify GPS trajectories. However, traditional algorithms focus on geometric characteristics of linear features. Trajectory data may record information beyond location. Examples include time and elevation, and inferred information such as speed, transportation mode, and activities. Effective trajectory simplification should preserve these characteristics in addition to location and orientation of spatial-temporal movement. This paper proposes an Enhanced Douglas–Peucker (EDP) algorithm that implements a set of Enhanced Spatial-Temporal Constraints (ESTC) when simplifying trajectory data. These constraints ensure that the essential properties of a trajectory be preserved through preserving critical points. Further, this study argues that speed profile can uniquely identify a trajectory and thus it can be used to evaluate the effectiveness of a trajectory simplification. The proposed ESTC-EDP simplification method is applied to two examples of GPS trajectory. The results of trajectory simplification are reported and compared with that from traditional DP algorithm. The effectiveness of simplification is evaluated.

During the last decades, concepts of Intelligent Transportation Systems (ITS) were continuously adapted and improved based on new insights into human travel behavior. Drivers for improvements are the quantity and quality of available mobility data, which increased significantly in recent years. Based on travel behavior, literature proposes a large number of different solutions for next step or future location prediction. However a holistic spatio-temporal prediction, which could further improve the quality of ITS, creates a more complex task. The prediction of medium-term mobility for one to seven days is challenging in particular for atypical travel behavior, since the weekdays’ order delivers no reliable indication for the next day’s travel behavior. With our contribution, we explore the benefits of various prediction approaches for medium-term mobility prediction and combine them dynamically to predict individual mobility behavior for a period of one week. The derived framework utilizes an exhaustive search approach to benefit from a machine learning based clustering method on location data. In conjunction with an Artificial Neural Network, the prediction framework is robust against prediction errors created by atypical behavior. With two data sets consisting of smartphone and vehicle data, we demonstrate the framework’s real-world applicability. We show that clustering an individual’s historical movement data can improve the prediction accuracy of different prediction methods that will be explained in detail and illustrate the interrelation of entropy and prediction accuracy.

Road intersection is an important part of the road network, and it plays a key role in many intelligent transportation applications. Currently, the generation of road maps required expensive field surveying and labor-intensive mapping. With more and more public vehicles equipped with positioning devices, massive trajectory data are available. These trajectory data provides a new way to generate and update urban road networks in real time. Although numerous methods are currently available to reconstruct road maps from GPS trajectories, most of these methods focus on the generation of road segments (i.e., road centerlines or road lanes). Road intersections are simply represented by nodes, the generation of road intersections with detailed structures still remains a challenge. Therefore, this paper proposes a novel method for constructing road intersections from vehicle GPS trajectories. First, the boundaries of road intersections are detected by turning angles clustering. Then, the entrances and exits of each intersection are identified based on a newly developed method. Finally, the detailed geometric structures of intersections are reconstructed based on the detected entrances and exits, and turning rules are further extracted and assigned to each intersection. The experiments on real-world trajectory data show that the proposed method can generate the detailed geometric structures of road intersections as well as the turning rule semantic information successfully.