Detecting Illegal Pickups of Intercity Buses from Their GPS Traces *

2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC) October 8-11, 2014. Qingdao, China Detecting Illegal Pickups of Intercity Buses from Their GPS Traces * Ling Yin, Jinxing Hu, Lian Huang, Fan Zhang, and Peng Ren Abstract—In some regions, especially in developing countries Therefore, this illegal pickup behavior has to be controlled and such as China and India, intercity bus drivers would like to pick stopped. Police Department in many cities of China put in a up passengers outside the scheduled bus stations and pocket the number of traffic enforcement units to investigate the illegal bus fares. Such illegal pickups bring large safety risks. The alert pickup locations and try to arrest bus drivers who are functions offered by the current vehicle tracking systems barely conducting illegal pickups on site. An effective site can catch this illegal act, and few studies of detecting driving investigation requires long-term experience from traffic police anomaly from GPS data focus on it either. This study hereby and takes large time and labor costs, which call for assistance presents an initial effort to fill the gap. We propose an approach from modern technology of intelligent transportation systems to automatically detecting suspicious pickup locations from (ITS). intercity bus GPS traces, and implement the approach in a geographical information system. A case study demonstrates the This study develops a geographical information system for effectiveness of the system with its high accuracy of detecting traffic police (GIS-TP) to automatically detect suspicious illegal pickup locations, and its functionality to help traffic pickup locations from intercity bus GPS traces. This type of police to understand illegal pickup behavior and plan a site application has not yet been found in related literature. Based investigation. on the near-repeat pattern of illegal pickups, the GIS-TP first I. INTRODUCTION uses a kernel density model to identify all stop clusters as a pool of suspicious pickup locations, and filters out As one of the most common methods of public unreasonable locations based on the cluster patterns of transportation, intercity bus services are of prime importance reasonable stops along with a series of spatial analyses. It also in many countries and regions. An intercity bus carries evaluates the suspicious level of each suspicious pickup passengers travelling significant distances between different location by analyzing its neighboring built environment and cities, towns, or other populated areas. Unlike an inner-city pickup behavior. The GIS-TP allows traffic police to visually transit bus service, which has frequent stops throughout a city examine each suspicious pickup location on map with the or town, an intercity bus service generally has a single stop at characteristics including suspicious level, total stops, most one station in or near a city, and travels long distances without likely pickup hours, and highly suspicious bus licenses. Such stops till next city station. However, in some countries or knowledge can improve the efficiency of site investigation. regions, especially in developing countries such as China and India, intercity bus drivers would like to pick up passengers at The remainder of this article consists of six parts. 1) random or selected locations outside the scheduled bus Section 2: a review of related work on anomaly detection from stations and pocket the bus fares. Since it costs lower than GPS traces; 2) Section 3: an overview of the GIS-TP design; regular bus fare and boarding locations could be selected, a 3) Section 4: a proposal of the approach to detecting illegal number of passengers would choose to board outside the bus pickup locations; 4) Section 5: a case study and a discussion of stations. It seems like a win-win situation for bus drivers and detection results; 5) Section 6: a demonstration of the some passengers, but gradually black markets of illegal web-based user interface of the GIS-TP; 6) Section 7: a intercity bus fares are growing in size and complexity. This summary of contributions and future work. however is against public transport regulations and is an II. RELATED WORK illegal act. Such illegal pickups act brings large safety risks. The extra passengers boarding outside bus stations would In recent years, GPS-based vehicle tracking systems cause bus overload, and probably carry hazardous items (e.g., (VTS) gradually become necessary equipment on intercity combustible and explosive materials) since these passengers buses. These VTS can monitor bus speed, idle duration, route, do not undergo the safety inspections at bus stations. Overload and other observations derived from GPS traces [1-2]. The and hazardous items are the major contributory factors that major automatic alert functions to detect driving irregularities cause bus accidents. Frequently stopping largely harms in current VTS include speeding alert, route deviation alert, passenger comfort for those who legally board at bus stations, and overtime stop alert [3-4]. These alert functions are based and may lead to bus delays or augmented road congestion. upon simple comparisons between direct observations from GPS data and regularities such as speed limit, scheduled route, and scheduled stop time. However, intercity buses often pick *Resrach supported by the National Natural Science Foundation of China up extra passengers along the scheduled route, which would (41301440, 61100220) and the Fundamental Research Foundation of not trigger the route deviation alert; extra passengers often Shenzhen (JCYJ20130401170306842). board on buses within a few minutes, which would not trigger Ling Yin, Jinxing Hu (corresponding author), Fan Zhang, Peng Ren are with the Shenzhen Institutes of Advanced Technology, Chinese Academy of the overtime stop alert. The current VTS thus can barely help Sciences, Shenzhen, China (phone: +86-755-86392373; fax: to catch the illegal pickups. +86-755-86392299; e-mail: {yinling; jinxing.hu; zhangfan; peng.ren}@ siat.ac.cn). Researchers have tried to use data mining techniques to Lian Huang is with the ShenZhen Transportation Operation Command support more accurate and intelligent anomaly detections from Center, Shenzhen, China (e-mail: [email protected]). U.S. Government work not protected by 2162 U.S. copyright GPS traces. [5] developed a conditional random field model to items within the stop event. It also calculates the duration of a detect anomaly in speed, drive direction, and possible traffic stop event. Because most illegal pickups only take a few jams from GPS traces. [6] and [7] respectively proposed minutes, we set a stop duration threshold Tstop to reduce the statistical models to monitor vehicle speed violation based on pool of suspicious stops. 3) An illegal pickup detection GPS traces. [8] and [9] used taxi GPS traces to detect taxi module: it infers suspicious pickup locations based on the stop driving fraud indicated by long-distance detour. [10] also used events. This module will be discussed in detail in the Section 4. taxi drivers’ detour behavior to infer traffic jam. [11] mined 4) A cloud computing platform: it is implemented by a the movement patterns from personal GPS data to detect multi-node Hadoop cluster, and supports the GPS data disorientation of elders. These proposed anomaly detection preprocessing module and the stop detection module. 5) A methods neither help to detect the illegal pickups. [12] and [13] GIS engine: it offers spatial analysis functions, and supports designed solutions for stay point detection from GPS traces. the illegal pickup detection module. 6) A GIS sever: it Their works however do not infer if the stay points are illegal provides web services for the detection results and maps. pickup locations or not. IV. ILLEGAL PICKUP LOCATION DETECTION BASED ON GPS This study is an effort to fill the gap to assist in detecting TRACES illegal pickup locations of intercity buses from their GPS traces. We design the approach to detecting illegal pickup locations based on typical illegal pickup patterns and the III. OVERVIEW OF THE GIS-TP DESIGN investigation focus from traffic police. Many intercity buses tend to choose the same or near pickup locations to attract GPS traces from intercity buses serve as an input to the more passengers. Such pickup locations will become fixed and GIS-TP. A GPS data item consists of five attributes: (ID, public ones in the black market. When the market grows to a latitude, longitude, speed, and time). ID is the unique license certain size, it might be discovered by traffic police. Then number of an intercity bus. Latitude and longitude define the these pickup locations will be changed. This illegal pickup geographical location of the bus at the time of data collection. pattern falls clearly the near-repeat phenomenon in Speed is the velocity of the bus at the time of data collection. criminology, which states that if a location is the target of a Time is the time stamp of the GPS data item. crime, then locations within a relatively short distance have an increased chance of being targets for a limited period of time [14-15]. Besides, traffic police focus on these pickup locations with a certain size instead of occasional and random ones. Therefore, the first step is to detect stop clusters with high density of stop events within a period of time. Step 1: Detecting Stop Clusters. To efficiently identify stop clusters and accurately extract the boundary of a spatial cluster, we use the kernel density estimation to generate a density map of stop events within a given period of time. Kernel density estimation is a statistically non-parametric way of inferring the probability density function of a population based on a finite data sample [16]. It is commonly used in spatial analysis to calculate a magnitude per unit area from point features using a kernel function to fit a smoothly tapered surface to each point. Because observed bus stop locations within a period of time can be considered a finite point data sample for calculating the probability density of all stop events, kernel density estimation is one suitable method to serve our objective.

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