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With the availability of Global Positioning System (GPS) receivers to capture vehicle location, it is now feasible to easily collect multiple days of travel data automatically. However, GPS-collected data are not ready for direct use in trip rate or route choice research until trip ends are identified within large GPS data streams. One common param...
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... return trip must have occurred in between. The third step was to find trips omitted by the participants in the log but identifiable in the GPS data stream (with the aid of GIS software to visualize the GPS data). The following additional criteria were used to flag trips not recorded by participants in their booklets: (i) Parallel parking tracks (Fig. 1), (ii) links used twice, once in each direction within a short time period (Fig. 2), (iii) approximately 180° heading changes (Fig. 2), (iv) GPS points off the road network (Fig. 3), and (v) an extraordinarily circuitous route between the start and end point of a trip recorded by the participant in the booklet (Fig. 4). Note here that ...
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Citations
... Location-based services (LBS) data contain the spatial locations of many mobile phone users, but they do not independently describe travel behavior (Du and Aultman-Hall 2007). A classification of the LBS data from raw location points to semantic activities is potentially desirable for many reasons, including removing error from travel diaries, improving the quality of travel models, and providing insights into traveler decisions (Bohte and Maat 2009;Usyukov 2017). ...
Location-based services data collected from mobile phones represent a potentially powerful source of travel behavior data, but transforming the location points into semantic activities – where and when activities occurred – is non-trivial. Existing algorithms to label activities require multiple parameters calibrated to a particular dataset. In this research, we apply a simulated annealing optimization procedure to identify the values of four parameters used in a density-based spatial clustering with additional noise and time entropy (DBSCAN-TE) algorithm. We develop a spatial accuracy scoring function to use in the calibration methodology and identify paths for future research.
... Identification of stop points There are two main approaches to identifying stop points from continuous GPS trajectory data: the time threshold and the clustering. By comparing the time difference between consecutive trajectory points with a given time threshold, we can identify whether a trajectory point is a stop point, which is the time threshold method (Du and Aultman-Hall 2007;Li et al. 2008). The clustering method aggregates some trajectory points into one stop point through a clustering model (Huang et al. 2020). ...
Understanding the characteristics of passenger vehicle use is the prerequisite for effective urban management. However, it has been challenging in the existing literature due to the lack of continuously observed data on passenger vehicle use. Thanks to the advances in data collection and processing techniques, multi-day vehicle trajectory data generated from volunteered passenger cars provide new opportunities for examining in depth how people travel in regular patterns. In this paper, based on a week's operation data of 6600 passenger cars in Shanghai, we develop a systematic approach for identifying trips and travel purposes, and classify vehicles into four categories using a Gaussian-Mixed-Model. A new method is proposed to identify vehicle travel regularities and we use the Z Test to explore differences in travel time and route choices between four types of vehicles. We find that commercially used vehicles present high travel intensity in temporal and spatial aspects and the use intensity in elevated roads is higher for household-used commuting vehicles than semi-commercially used vehicles. The methodologies and conclusions of this paper may provide not only theoretical support for future urban traffic prediction, but also guidance for employing customized active traffic demand management measures to alleviate traffic congestion.
... Although the accuracy has been improved, there are regional limitations, the application scenarios are limited to the road sections studied in the literature, and they do not have universality [9]. Du et al. used a combination of truck dwell time, distance to the road network and heading variation to identify the endpoint of the truck's journey, reflecting information about the nature of the parking incident [10]. Under the condition of big data, the course change may be affected by the road topology, resulting in incorrect classification [8]. ...
Based on the GPS trajectory data of a freight enterprise in Dalian, China, this paper studies the identification of loading and unloading points by a clustering algorithm. Firstly, by analysing the characteristics of freight loading and unloading behaviour, combined with the spatial and temporal distribution characteristics of truck GPS trajectory data, three characteristic variables of the number of trucks passing through a certain place, the average speed of trucks and the average stay time of trucks in the place are extracted. Then, the clustering algorithm and visual analysis are used to obtain the target cluster, and the POI language of the geographic information is obtained according to the points in the target cluster. The meaning information is crawled to accurately identify the result of the freight loading point. Finally, two classical clustering algorithms, K-means and GMM, are evaluated and compared. The results show that the identification method designed in this paper finally identifies 2,320 freight loading and unloading points from 11,406,000 trajectory data, which can realise the accurate extraction of freight loading and unloading points.
... Notably, the definition of the dwell time threshold is crucial for stop identification because values that are too long or too short could miss stops and/or incorrectly segment trips. Setting the appropriate dwell time threshold depends on the traffic conditions in a given area [40], and this explains substantial differences observed in the literature, with thresholds equal to 2 [36], 3 [41], 5 [15] and 15 minutes [42]. The second step typically applied a broad range of methods to differentiate trip-end stops from stops caused by congestion or other factors that are less important from the perspective of identifying logistics-related information. ...
The increasing wealth of truck global positioning system (GPS) data has broadened the opportunities for understanding freight logistics activities and enhancing research capabilities to real-world case studies. A very important piece of information from the planning and regulatory perspective concerns the occurrence and location of rest stops. In this study, we propose a data-driven unsupervised machine learning method to impute truck stop events by using a Continuous Hidden Markov Model (CHMM). Specifically, we estimate the joint probability distribution of a mixture of multivariate Gaussian densities, whose parameters depend on the latent states of a Markov chain. Each density represents a cluster of stops that are identified not only from their spatial proximity but also from their temporal proximity as the clustering of the rest stops depends on latent states that are conditional on expected times retrieved from the observed data. In this study, we applied the proposed method to a database containing more than 71 million GPS records of Australian trucks, and we particularly aimed to identify rest stops based on a list of features related to the locations and the load of the trucks. The results showed that the CHMM could account for the location proximity for different activities of truck drivers, and they were validated against complementary data on truck loads and land use by using a stratified sampling technique. Validation results indicated that 94.1% of the rest stops were correctly identified, and highlighted the advantage of the proposed approach without any requirement of labelled data, driver logbook or complimentary survey.
... In addition, Gong et al. used density-167 based clustering algorithms to extract candidate stops [6]. 168 Du and Aultman-Hall checked candidate activities under the 169 assumption that they occurred at a place away from the road 170 network [16]. ...
Travel data is vital for understanding individual travel behaviors and estimating travel demand. Compared with traditional travel surveys in which respondents were asked to recall their trips, smartphones can record GPS trajectories actively at a high level of accuracy in both space and time. However, how to identify trips and detect travel modes from raw GPS data in an efficient way is still challenging. To address these issues, we firstly target high-accuracy data collection through developing a smartphone-based person travel survey system which comprises of three components, i.e., data collector, data processor, and data validator. The data collector is a smartphone app for collecting GPS trajectories; the data processor is a server embedded with rule-based algorithms to extract travel-activity information; and the data validator is a webpage to present the self-extracted travel information for respondents’ validation. Secondly, we propose a hybrid random forest and merging algorithm to detect multiple travel modes in complex trips. The algorithm reaches an accuracy of 100% for simple trips and 95.7% for multimodal trips. These results indicate that the proposed hybrid random forest and merging algorithm can help identify travel modes of complex trips more accurately and efficiently.
... as it presents advantages over traditional measuring tools such as traffic cameras, loop detectors, and others [5]. However, GPS data has limitations, like spatial inaccuracy caused by urban canyons or misleading information caused by signal loss [5]. ...
... as it presents advantages over traditional measuring tools such as traffic cameras, loop detectors, and others [5]. However, GPS data has limitations, like spatial inaccuracy caused by urban canyons or misleading information caused by signal loss [5]. Furthermore, [6] states that access to large GPS databases could benefit emerging countries where data collection is rarely common. ...
... GPS devices record information such as latitude, longitude, speed, heading, altitude, engine status, etc. In addition, GPS allows different settings, such as record frequency and accuracy [5]. Data acquisition changed exponentially with the availability of the GPS devices allowing to measure traffic data as records in sequences of discrete positioning signals transmitted by the cars' GPS [4]. ...
This study analyzes urban freight GPS data to achieve more efficient public policies and better last-mile operations planning. The main objective is to obtain the time stops of each urban freight vehicle and classify the stop as traffic, delivery, or rest. After classifying each stop using data mining algorithms and applying machine learning models such as K-Means and HDBSCAN, the study demonstrates that it is possible to apply machine learning models capable of grouping GPS stops by similar features. The clusters obtained are relevant inputs to future research and potential applications, such as in public policy decision making or routing optimization allowing private and public entities to optimize urban logistics and last-mile operations.KeywordsMachine learningUrban logisticsLast mile operationsGeospatial data analysisK-meansHDBSCANCluster
... For example, Marchal et al. [48] and Bierlaire et al. [49] proposed a map matching algorithm to identify the routes followed by users. The same problem is tackled in Du and Aultman-Hall [50] and in Chung and Shalaby [51], which proposed some algorithms able to identify the routes and their origin and destination. ...
The path choice models play a key role in transportation engineering, especially when coupled with an assignment procedure allowing link flows to be obtained. Their implementation could be complex and resource-consuming. In particular, such a task consists of several stages, including (1) the collection of a large set of data from surveys to infer users’ path choices and (2) the definition of a model able to reproduce users’ choice behaviors. Nowadays, stage (1) can be improved using floating car data (FCD), which allow one to obtain a reliable dataset of paths. In relation to stage (2), different structures of models are available; however, a compromise has to be found between the model’s ability to reproduce the observed paths (including the ability to forecast the future path choices) and its applicability in real contexts (in addition to guaranteeing the robustness of the assignment procedure). Therefore, the aim of this paper is to explore the opportunities offered by FCD to calibrate a path/route choice model to be included in a general procedure for scenario assessment. The proposed methodology is applied to passenger and freight transport case studies. Significant results are obtained showing the opportunities offered by FCD in supporting path choice simulation. Moreover, the characteristics of the model make it easily applicable and exportable to other contexts.
... Great effort has been made to identify the trip ends automatically from tabular GPS data. In previous studies, the most common way to break the continuous GPS data into trip-based data segments is to search stop dwells [7]. Depending on different GPS settings, a valid stop dwell can be defined by 1) speeds that are close to zero (e.g. less than 5 km/h) over a predefined time in the ongoing GPS recording; or 2) a gap between two consecutive timestamps greater than a predefined time due to signal loss or device outage [8]. ...
The advancement of data collection technologies has brought an upsurge in GPS applications. For example, travel behaviour research has benefited from the integration of multiple sources of Global Positioning System (GPS) data. However, the effective use of such data is still impeded by the challenge in data processing. For instance, GPS data, despite providing detailed spatial movement information, do not label the starting and finishing points of a trip, especially for commercial trucks. Hence, there is a critical need to develop a trip identification method to effectively use the trajectory data provided by GPS without additional information. This paper focused on identifying trips from the raw GPS data. Specifically, a systematic method is proposed to extract trips on the basis of origin-destination (OD) pairs by using a 5-step procedure. An application was provided on estimating the performance of travel time reliability using three metrics based on the OD trips for each dedicated truck. The application showed that, in general, trucks on long-distance routes have less reliable travel times compared to trucks on short-distance routes. This paper provides an example of using GPS data, without further information, to study travel time for freight performance and similar needs of punctuality in logistics.
... e setting criterion of the stationary point is that the speed is clearly slower than the walking speed (V w ). e speed threshold of the stationary point is set to 0.51 m/s, and V w � 1.34 m/s refers to the parameters in Du [22]. To avoid contingency, this paper assumes that when the average speed of two consecutive data points is less than 0.51 m/s, the previous data point is a stationary point. ...
... (3) − (6)，(13), (16), (22), (23), ...
Carsharing is regarded as a new mode of transportation that can meet the diversity of travel demands. Carsharing systems have different operating modes, and one-way systems are more widely used since cars can be dropped off at any station. However, their planning involves a series of joint decisions regarding the number, size, and location of stations, as well as the fleet size. This paper develops a data-driven mixed-integer linear programming (MILP) model for planning one-way carsharing systems that consider the spatial distribution of demand and the interacting decisions between stations. The characteristics of existing stations and their spatiotemporal correlations are an important part of the model. To solve the MILP model, the extension of the Benders decomposition algorithm is adopted. The practicality of the proposed approach is demonstrated in a case study in Beijing, China. The results show that the existing planning of carsharing could result in a serious waste of resources. In contrast, the proposed method can obtain effective results in a reasonable time. The location results corresponding to a different rate of satisfied demand show that increasing the parking spots to improve the interaction between stations can effectively reduce the cost of operations. It should be noted that this paper only considers the benefit of operators. Future works will be carried out to optimize the one-way carsharing system by considering the benefits of operators, as well as the benefits of users and society. In addition, the impact of COVID-19 will be taken into account in future modeling and case studies.
... In order to identify the vehicle freight activities from GPS data (e.g., stops), algorithms have to be developed [33,34]. Vehicle stop identification is a key aspect faced when analyzing the data, that can be very hard when intermediate short stops (e.g., waiting at traffic lights, congestion) are detected [33,[35][36][37]. In this topic, Holguín-Veras et al. [34] proposed a procedure based on changes in speed and acceleration to identify the stops of freight vehicles, while Alho et al. [33] compared some algorithms developed with the aim of identifying the tour stops. ...
This paper investigates the opportunities offered by floating car data (FCD) to infer delivering activities. A discrete trip-chain order model (within the random utility theory) for light goods vehicles (laden weight less than 3.5 tons) is hence proposed, which characterizes delivery tours in terms of the number of stops/deliveries performed. Thus, the main goal of the study is to calibrate a discrete choice model to estimate the number of stops/deliveries per tour by using FCD, which can be incorporated in a planning procedure for obtaining a preliminary assessment of parking demand. The data used refer to light goods vehicles operating in the Veneto region. The database contains more than 8000 tours undertaken in 60 working days. Satisfactory results have been obtained in terms of tour estimation and model transferability.
