Real-time Vehicle Location Research Articles

A Cluster Based Feasible Time Interval for Tracking Lost or Stolen Vehicle

The system for tracking and monitoring lost or stolen vehicle is challenging. This device is widely used to assess the vehicle's location using GPS technology. It can be used to track a vehicle or vehicle fleet and to obtain information about the vehicle's current location. There are various challenges for tracking and monitoring vehicles and finding lost vehicles due to the lack of proper real-time vehicle location and hence it is difficult to take necessary action in the immediate proper time after the vehicle has lost or stolen. In this paper a Cluster Based Feasible Time Interval for Vehicle Tracking (CFTVT) algorithm for measuring the minimum time interval for taking action after the vehicle has lost or stolen is proposed. This proposed model helps to imply any appropriate vehicle tracking algorithm in the exact proper time after the vehicle has been lost or stolen.

UHF-RFID-Based Real-Time Vehicle Localization in GPS-Less Environments

The vehicle localization, which aims to identify a vehicle and then position the vehicle with a high precision, can be used to facilitate various applications and services in vehicular networks. Unfortunately, conventional localization systems, e.g., global positioning system (GPS), hardly meet the accuracy requirements especially in certain specific scenarios, such as tunnels. At the same time, Ultrahigh frequency (UHF) radio frequency identification (RFID) has become an efficient booster for internet of things (IoT) due to the desirable advantages, such as low cost, battery-free, and unique identification. In this paper, based on the UHF-RFID, we propose a novel real-time vehicle localization scheme in GPS-Less Environments. Considering the practical implementation of multiple RFID reader antennas on a vehicle is constrained, we adopt single antenna multi-frequency ranging scheme, in which the integer ambiguity problem is solved by the maximum-likelihood estimation (MLE)-based robust Chinese remainder theorem (CRT). With the reconstructed distances between the tags and the reader, the coordinates of the vehicle then can be calculated with the Levenberg-Marquardt (LM) algorithm. Furthermore, the computational complexities of the algorithms and the time consumption of the proposed scheme are analyzed. The experimental results demonstrate that the proposed scheme can track vehicle’s location with error lower than 27 cm at the probability of 90%.

Tracking and Monitoring Technologies to Support Airport Construction Safety

This paper discusses how advanced tracking and monitoring technologies can be used to increase safety for construction activities at airports. Airport construction is a significant infrastructure sector and in 2020 the Airport Improvement Program funded $300 million for airport projects in the United States. This includes funding for pavement reconstruction, airfield upgrades to meet current standards (e.g., wider runways), the construction of new terminal facilities, and additional capacity (e.g., new runways). The airfield environment presents special hazards for constructors, including proximity to active aircraft operations, and the requirement to comply with many additional rules such as those required by Federal Aviation Administration (FAA) and the Transportation Security Agency (TSA); many of these rules are required to ensure safety and security. Airport construction activities require close oversight, additional inspections by airport operations personnel, and stringent rules regarding access and operation in different areas of the airfield. Access and operating requirements affect both construction workers and construction equipment, and there are often different rules for different areas at the airport. One of the most restricted areas on an airport is the runway. Access to the runway and the safety area around a runway requires explicit permission from Air Traffic Control (ATC). If a vehicle or pedestrian (or aircraft) crosses the boundary without permission, it is considered a runway incursion. A runway incursion could cause a major incident at the airport and could result in injuries and fatalities. Runway incursion events are recorded by the FAA and represent a serious safety violation for the airport. Runway incursions and the technology to prevent incursions are discussed as an example that illustrates the need for and benefits of accurate monitoring of construction equipment and personnel. Technologies that may be used to enhance tracking and support situational awareness are present to illustrate the possible ways that enhanced monitoring can be used at an airport. Solutions may include the use of advanced technologies such as real-time vehicle location sensors, which may be combined with robust electronic maps to enhance safety for construction equipment and personnel location in the airfield environment. A discussion of opportunities and challenges of these systems is detailed.

Inference of HDVs real-time locations in mixed autonomous traffic flow scenario

In the near future, the road traffic flow will consist of both human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs). Since HDVs cannot communicate with CAVs and road side units (RSU), they are unobservable to CAVs if outside the range of sensing. In such case, the advantages of CAVs will be compromised and various high-level tasks in mixed autonomous traffic flow cannot be achieved. This study proposes a model to infer HDVs information using sensing data of CAVs. The rationale is that CAVs react to HDVs based on the car-following (CF) logic. Inversely, real-time locations of HDVs can be reconstructed using the data from CAVs. The Bayesian network is used to reflect the CF logic and develop a real-time vehicle location inference method for both single and multiple CAVs scenarios. Last, the method is tested using real-world dataset. Both time consumption and near-field estimation precision are validated.

Transit Accessibility Measurement Considering Behavioral Adaptations to Reliability

Accessibility measures are necessary for evaluating the benefits of proposed transportation improvements. However, they often do not account for travel time unreliability, but instead incorporate deterministic and time-invariant travel times. This approach risks mischaracterizing the accessibility experienced by travelers. In this paper, we review recent literature on accessibility and travel time reliability with a focus on transit and introduce an approach to joint accessibility-reliability measurement that relies on a behavioral perspective. Using this behavioral perspective, we propose that existing accessibility measures be implemented using travelers’ total travel time budget as a measure of travel time, and that varying departure time strategies depending on service characteristics be considered. The total travel time budget can reasonably be quantified with a high percentile of the total travel time distribution. However, we note that different percentiles may be more appropriate for different traveler types, as these percentiles correspond to varying tolerances for late arrivals. This behavioral perspective can be operationalized with commonly used accessibility measures, such as the cumulative opportunity measure, and with real-time vehicle location data. We include a demonstration of the potential changes in accessibility estimates when accounting for travel time unreliability, with a simplified case study of a transit route in San Francisco. The results show a considerable reduction of the number of opportunities available to travelers when the calculation is based on the latter—between 5.9% and 37.9% less, depending on various factors. Such differences have the potential to significantly affect the accessibility benefits of transit capital investments.

Measuring risk of missing transfers in public transit systems using high-resolution schedule and real-time bus location data

The emergence of urban Big Data creates new opportunities for a deeper understanding of transportation within cities, revealing patterns and dynamics that were previously hidden. Public transit agencies are collecting and publishing high-resolution schedule and real-time vehicle location data to help users schedule trips and navigate the system. We can use these data to generate new insights into public transit delays, a major source of user dissatisfaction. Leveraging open General Transit Feed Specification (GTFS) and administrative Automatic Passenger Counter (APC) data, we develop two measures to assess the risk of missing bus route transfers and the consequent time penalties due to delays. Risk of Missing Transfers (RoMT) measures the empirical probability of missed transfers, and Average Total Time Penalty (ATTP) shows overall time loss compared to the schedule. We apply these measures to data from the Central Ohio Transit Authority (COTA), a public transit agency serving the Columbus, Ohio, USA metropolitan area. We aggregate, visualise and analyse these measures at different spatial and temporal resolutions, revealing patterns that demonstrate the heterogeneous impacts of bus delays. We also simulate the impacts of dedicated bus lanes reducing missing risk and time penalties. Results demonstrate the effectiveness of measures based on high-resolution schedule and real-time vehicle location data to assess the impacts of delays and to guide planning and decision making that can improve on-time performance.

Location of Underground Parking Lot Based on Bluetooth Ibeacon Technology

With the popularity of electric vehicles in China, how to guide the vehicle to the charging facility in the underground garage has become an urgent problem to be solved. Among them, the realization of real-time vehicle location is one of the keys. In this paper, the location fingerprint scheme based on iBeacon technology is adopted, and the improved k-proximity algorithm is combined to realize the positioning.

Monocular Localization with Vector HD Map (MLVHM): A Low-Cost Method for Commercial IVs.

Real-time vehicle localization (i.e., position and orientation estimation in the world coordinate system) with high accuracy is the fundamental function of an intelligent vehicle (IV) system. In the process of commercialization of IVs, many car manufacturers attempt to avoid high-cost sensor systems (e.g., RTK GNSS and LiDAR) in favor of low-cost optical sensors such as cameras. The same cost-saving strategy also gives rise to an increasing number of vehicles equipped with High Definition (HD) maps. Rooted upon these existing technologies, this article presents the concept of Monocular Localization with Vector HD Map (MLVHM), a novel camera-based map-matching method that efficiently aligns semantic-level geometric features in-camera acquired frames against the vector HD map in order to achieve high-precision vehicle absolute localization with minimal cost. The semantic features are delicately chosen for the ease of map vector alignment as well as for the resiliency against occlusion and fluctuation in illumination. The effective data association method in MLVHM serves as the basis for the camera position estimation by minimizing feature re-projection errors, and the frame-to-frame motion fusion is further introduced for reliable localization results. Experiments have shown that MLVHM can achieve high-precision vehicle localization with an RMSE of 24 cm with no cumulative error. In addition, we use low-cost on-board sensors and light-weight HD maps to achieve or even exceed the accuracy of existing map-matching algorithms.

A Vehicle Tracking System Using Greedy Forwarding Algorithms for Public Transportation in Urban Arterial

A vehicle tracking system assists public transportation users in their movements by providing real-time information on the locations of vehicles in transit. Public transportation in parts of developing nations, especially Nigeria is ineffective. The system is chaotic and frustrating, especially at peak traffic periods. In a bid to address the problem, a vehicle tracking system was developed as a component of an Advanced Public Transportation System to improve commuting in an urban arterial. The developed system is based on wireless technologies of the Global Positioning System (GPS) and Global System for Mobile Communication module. It records and displays real-time vehicle location using a GPS-based greedy forwarding algorithm, computes route distance information using distance-time based algorithm and radar range sensor (RRS). A pseudo-range mathematical model using the Haversine formula was adopted in determining the accurate position of an object during signal transmission from GPS satellites to the receiver message module. The minimum inversion matrix method was used for the GPS-based geometric dilution of precision (GDoP) selection of satellite approximation and distance. Atmega328P controller chip was used as the logical control unit for processing activities in the tracking system and programming in Arduino IDE using C-language. The system was deployed to a university transportation system in Nigeria: a journey to and from the Bosso and Gidan Kwano Campuses route in the Federal University of Technology, Minna. The vehicle tracking system was tested with 11 tracked satellite and minor dilution error (PDoP error = 1.9, HDoP=0.9, and VDOP=1.7) was recorded. The system is efficient and accurate in distance and time information display with a minor delay. The system would enhance fleet management schemes for urban arterial and can be adopted universally.

자동차용 블랙박스를 이용한 실시간 차량위치추적 시스템의 구현

본 연구는 블랙박스 영상을 이용하여 도주 중인 범죄차량의 검거나 원하는 중요한 영상을 확보하는 것을 목적으로 하는 실시간 차량 위치추적 시스템을 구현한다. 차량에 장착된 블랙박스의 카메라를 통해 5초 간격으로 전방 차량의 영상을 찍은 다음 영상 내부의 번호판을 인식한다. 인식된 차량번호는 문자열의 형태로 운전자의 스마트폰으로 전송되며 스마트폰의 현재 GPS 값과 합쳐 서버로 전송한다. 그리고 서버는 전송받은 데이터들을 데이터베이스 관리 시스템을 통해 처리하여 중복을 방지하고 저장한다. 위치 추적 프로그램은 사용자가 원하는 차량의 위치 정보를 서버에게 요청하여 수신하고 그래픽유저 인터페이스에 표시한다. 사용자는 이 프로그램으로 특정 차량의 일정 시간동안 이동 경로 또는 특정 위치를 지나간 차량들의 정보를 얻을 수 있다.

A cost-effective decentralized vehicle remote positioning and tracking system using BeiDou Navigation Satellite System and Mobile Network

Most existing production vehicle tracking services rely on the Global Positioning System (GPS) and a proprietary server/client infrastructure. This type of inflexible and centralized architecture incurs severe vender-dependency and high service cost. In this paper, we propose a cost-effective decentralized vehicle remote positioning and tracking system architecture. This architecture only consists of two components: (1) a vehicle terminal for collecting real-time vehicle location from a navigation satellite system and report it to a user terminal via the existing mobile network and (2) a user terminal on a mobile phone for analyzing the location information from (1) and inform the owner. We prototype the positioning and tracking system using the BeiDou Navigation Satellite System (BDS) and Global System for Mobile Communication(GSM) Network. Experiment results illustrate that the system is capable of tracking vehicle location and warning the owner if the vehicle unexpectedly moves more than 100 m at low cost. With maximum vender-neutrality and high-availability as the design philosophy, the system is able to utilize multiple available navigation satellite systems and mobile networks.

Assessing public transit performance using real-time data: spatiotemporal patterns of bus operation delays in Columbus, Ohio, USA

ABSTRACTPublic transit vehicles such as buses operate within shared transportation networks subject to dynamic conditions and disruptions such as traffic congestion. The operational delays caused by these conditions can propagate downstream through scheduled transit routes, affecting system performance beyond the initial delay. This paper develops an approach to measuring and assessing vehicle delay propagation in public transit systems. We fuse data on scheduled bus service with real-time vehicle location data to measure the originating, cascading and recovery locations of delay events across space with respect to time. We integrate the resulting patterns to construct stop-specific delay propagation networks. We also analyze the spatiotemporal patterns of propagating delays using parameters such as 1) transit line-based network distance, 2) total propagating delay size, and 3) distance decay. We apply our methodology using publicly available schedule and real-time location data from the Central Ohio Transit Authority (COTA) public bus system in Columbus, Ohio, USA. We find that delay initiation is spatially and temporally uneven, concentrating on specific stops in downtown and specific suburban locations. Core stops play a critical role in propagating delays to a wide range of connected stops, eventually having a disproportional impact on the on-time performance of the bus system.

Robust Drivable Road Region Detection for Fixed-Route Autonomous Vehicles Using Map-Fusion Images.

Environment perception is one of the major issues in autonomous driving systems. In particular, effective and robust drivable road region detection still remains a challenge to be addressed for autonomous vehicles in multi-lane roads, intersections and unstructured road environments. In this paper, a computer vision and neural networks-based drivable road region detection approach is proposed for fixed-route autonomous vehicles (e.g., shuttles, buses and other vehicles operating on fixed routes), using a vehicle-mounted camera, route map and real-time vehicle location. The key idea of the proposed approach is to fuse an image with its corresponding local route map to obtain the map-fusion image (MFI) where the information of the image and route map act as complementary to each other. The information of the image can be utilized in road regions with rich features, while local route map acts as critical heuristics that enable robust drivable road region detection in areas without clear lane marking or borders. A neural network model constructed upon the Convolutional Neural Networks (CNNs), namely FCN-VGG16, is utilized to extract the drivable road region from the fused MFI. The proposed approach is validated using real-world driving scenario videos captured by an industrial camera mounted on a testing vehicle. Experiments demonstrate that the proposed approach outperforms the conventional approach which uses non-fused images in terms of detection accuracy and robustness, and it achieves desirable robustness against undesirable illumination conditions and pavement appearance, as well as projection and map-fusion errors.

Leveraging Smartphones for Vehicle Lane-Level Localization on Highways

When vehicle road-level localization cannot satisfy people's need for convenience and safety driving, lane-level localization becomes a corner stone in Intelligent Transportation System. Existing works of tracking vehicles on lane-level mostly depend on pre-deployed infrastructures and additional hardwares. In this paper, we utilize smartphones to sense driving conditions for vehicle lane-level localization on highways. By analyzing driving traces collected from real driving environments, we find that each type of lane-change has its unique pattern on the vehicle's lateral acceleration. Based on this observation, we propose a Lane-Level Localization ( $L^3$ ) system, which can perform real-time vehicle localization on lane-level only using smartphones when vehicles are driving on highways. Our system first uses embedded sensors in smartphones to capture the patterns of lane-change behaviors. Then, a Finite State Machine is employed to track vehicles on lane-level leveraging the patterns. Extensive experiments demonstrate that $L^3$ is accurate and robust in real driving environments. The experimental results show that, on average, $L^3$ achieves the accuracy of 91.49 percent on lane change detection and 90.31 percent on lane-level localization.

Localization of a Vehicle: A Dynamic Interval Constraint Satisfaction Problem-Based Approach

This paper introduces a new interval constraint propagation (ICP) approach dealing with the real-time vehicle localization problem. Bayesian methods like extended Kalman filter (EKF) are classically used to achieve vehicle localization. ICP is an alternative which provides guaranteed localization results rather than probabilities. Our approach assumes that all models and measurement errors are bounded within known limits without any other hypotheses on the probability distribution. The proposed algorithm uses a low-level consistency algorithm and has been validated with an outdoor vehicle equipped with a GPS receiver, a gyro, and odometers. Results have been compared to EKF and other ICP methods such as hull consistency (HC4) and 3-bound (3B) algorithms. Both consistencies of EKF and our algorithm have been experimentally studied.

Logistics Vehicle Monitoring Intelligence System

In this paper the design and the manufacture of the logistics vehicle monitoring system based on ARM11 is proposed. Camera module, sensor module, barcode scanning module, GPS positioning module, wireless communication module, and GPRS module are employed on the terminal, which use LINUX and ARM11 to realize software system and hardware system separately, to make the system more practical and convenient. In data acquisition part, data including vehicle interior images, vehicle inclination angle and goods weight, goods information obtained by the camera module acquisition, the sensor module and bar code scanner module separately is sent to the monitoring terminal via a wireless communication module for display after data processing. Monitor terminal display real-time vehicle location information collected by GPS module, and at the same time, send location information to the server via GPRS module, displaying on the electronic map. Thus we realize the real-time monitoring of vehicles and logistics management.

Constructing a routable retrospective transit timetable from a real-time vehicle location feed and GTFS

We describe a method for retroactively improving the accuracy of a General Transit Feed Specification (GTFS) package by using a real-time vehicle location data set provided by the transit agency. Once modified, the GTFS package contains the observed rather than the scheduled transit operations and can be used in research assessing network performance, reliability and accessibility. We offer a case study using data from the Toronto Transit Commission and find that substantial aggregate accessibility differences exist between scheduled and observed services. This ‘error’ in the scheduled GTFS data may have implications for many types of measurements commonly derived from GTFS data.

Discovering the space–time dimensions of schedule padding and delay from GTFS and real-time transit data

Schedule padding is the extra time added to transit schedules to reduce the risk of delay. Where there is more random delay, there should be more schedule padding. While schedule padding is a product of transit planners, a method for detecting when and where it exists could provide valuable feedback as transit agencies continually develop their networks. By analyzing transit schedules and real-time vehicle location data at the level of stop-to-stop segments, we can locate padding in space and time and identify the places that may be most effected by stochastic delay. Such information could be used to target delay-reduction interventions such as fare prepayment or transit-only rights of way. The Toronto Transit Commission is used as a case study, and initial results suggest that highly delayed segments appear mostly in the expected, but some surprising, places.

Constructing a Routable Transit Network from a Real-time Vehicle Location Feed

GIScience 2016 Short Paper Proceedings Constructing a Routable Transit Network from a Real-time Vehicle Location Feed Nate Wessel 1 , Jeff Allen 1 , Steven Farber 2 Department of Geography and Planning, University of Toronto, Sidney Smith Hall, 100 St. George Street, Toronto, ON M5S 3G3, Canada Email: {nate.wessel; jeff.allen}@mail.utoronto.ca Department of Human Geography, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON M1C 1A4, Canada Email: steven.farber@utoronto.ca Abstract Many transit agencies publish open access feeds of real-time fleet locations for use by application developers to deliver mobile navigation services. However, by collecting a transit data feed over time, we demonstrate how to recreate retrospective, routable transit networks that are useful in answering network performance and accessibility research questions. In this research we develop an end-to-end GIS toolchain for 1) downloading and storing a transit data feed, 2) coercing the collected spatiotemporal database into a retrospective transit schedule adhering to the General Transit Feed Specification (GTFS) data standard, and 3) creating a routable transit network with time-dependent travel times in OpenTripPlanner. We further demonstrate how this toolchain can be used to identify discrepancies between scheduled and actual travel times on the network and motivate the usefulness of this approach through an accessibility analysis. 1. Introduction The emergence of the General Transit Feed Specification (GTFS) data standard, and the publication of GTFS packages by thousands of agencies worldwide has unleashed a flurry of tools development for researching transit networks (Hadas and Ranjitkar 2012), transit travel times (Farber et al. 2014), and time-dependent accessibility metrics (Fransen et al. 2015; Owen and Levinson 2015; Farber et al. 2016). One problem with travel time research based on GTFS schedules is that this format implicitly ignores inaccuracies in travel times due to, among other causes, operational delays, service interruptions or unrealistic schedules. Researchers requiring more accurate measures of transit travel times have relied on field measurements and large-scale simulation models of multimodal network assignments. But these sources of data are expensive to collect, may take years to implement, may not be very accurate, and are difficult to implement on a large network, in continuous time, and in perpetuity. To account for these shortcomings, we put forward a new methodology that capitalizes on freely available vehicle location data feeds to produce a routable retrospective transit network using open source tools. This network contains actual travel times that are true to observed transit network performance, not the expected performance contained in the official transit schedules. In this paper we will describe the toolchain we developed and demonstrate its use in detecting service that differs from the schedule, as well as the implications of these differences for end-to-end travel times and time-dependent accessibility scores. 2. Data and Methods This research uses two primary datasets, a current GTFS package for the Toronto Transit Commission (TTC) and a 48-hour extract of all vehicle locations reported by the TTC-NextBus

AVL and Monitoring for Massive Traffic Control System over DDS

This paper proposes a real-time Automatic Vehicle Location (AVL) and monitoring system for traffic control of pilgrims coming towards the city of Makkah in Saudi Arabia based on Data Distribution Service (DDS) specified by the Object Management Group (OMG). DDS based middleware employs Real-Time Publish/Subscribe (RTPS) protocol that implements many-to-many communication paradigm suitable in massive traffic control applications. Using this middleware approach, we are able to locate and track huge number of mobile vehicles and identify all passengers in real-time who are coming to perform annual Hajj. For validation of our proposed framework, various performance matrices are examined over WLAN using DDS. Results show that DDS based middleware can meet real-time requirements in large-scale AVL environment.