Probe Cars Research Articles

Potential of vision-enhanced floating car data for urban traffic estimation

Floating car data (FCD) have recently become a popular tool of urban traffic engineering. Conventional FCD contains a series of probe cars’ timestamped locations and are used to estimate traffic speeds and travel times and identify congestions. In this study, we propose the enhancement of conventional FCD with car vision information: traffic measurements, collected by video cameras or lidars, installed on the probe cars. Given the limited penetration rates of probe cars, such vision information can significantly improve the accuracy of traffic estimation. Our experimental study is based on two simulated vision-enhanced FCD data sets: sensor-based real-world traffic data set with simulated observability and a simulation model with vision-equipped probe cars. We estimate the potential of vision-enhanced FCD for urban traffic flow estimation for different probe car penetration rates. A recently proposed temporal geometric matrix completion algorithm is utilized for traffic speed estimation given incomplete spatiotemporal traffic flow information. Empirical results show that the availability of vision-enhanced FCD leads to significant improvement: the coverage of 6% of spatiotemporal slots by probe cars gives reasonable, and the coverage of 9% – nearly optimal accuracy of traffic speed estimation. Thus, obtained experimental results support our hypothesis about the utility of vision-enhanced FCD to improve traffic estimation accuracy and discover related problems and limitations.

Reconstructing vehicle trajectories on freeways based on motion detection data of connected and automated vehicles

Determining the trajectories of all vehicles on freeways is a challenging yet critical topic as trajectories reflect the characteristics of traffic flow and serve as a good basis for traffic management and control. With the advances of mobile sensing technology, connected and automated vehicles (CAVs) as a new source of probe car can provide high-resolution sampled trajectory data. Furthermore, as CAVs sense the surrounding traffic situation, they can offer information to understand the vehicle motions around them. Utilizing the data from CAVs thus supports the trajectory reconstruction of fully-sampled traffic flow and enables sophisticated evaluation of traffic states. This study develops a CAV detection data-based trajectory reconstruction method for freeway traffic. First, the intelligent driver model (IDM) is used to judge the motion of undetected human-driven vehicles (HV) between trajectories. The undetected vehicles will be inserted in traffic flow with the position and speed estimated by a modified IDM model. Subsequently, the complete trajectories of the inserted HVs will be reconstructed by IDM. Last, the validity of the method is verified by both simulation and empirical experiments. The results demonstrate the proposed method enables sufficient reconstruction of vehicle trajectories under different traffic densities and penetration rates of CAVs.

Crowdsourcing-Based Road Surface Evaluation and Indexing

Continuous monitoring of road surface quality is necessary to maintain high functionality of the entire road networks and allow users to perceive road roughness. Although several fundamental methods have been used to evaluate road roughness by employing smart probe cars, the accuracy of these methods is affected by factors, such as vehicle speed, sensor positions, and vehicle suspension systems. Also, the long-term application of these methods to the entire road network is also limited due to the high cost. Further, the widely used roughness indices do not reflect road users’ perception about road roughness as these indices are also affected by the accuracy of the roughness evaluation methods. Therefore, we propose a crowdsourcing based road roughness evaluation model which uses power spectral density accompanied with blind source separation technique to eliminate the vehicle effects. We also propose a road surface ranking based on majority voting algorithm for comparing roads based on their surface quality. Finally, the road surface roughness index is derived to widen the range of quality scale and capture roughness at fine granularity. The models are tested by real world experiments on different roads in Taiwan. The results show that the proposed model can accurately measure the roughness of roads, rank these roads and index them accordingly in a way that shows tiny differences in the road surface quality.

Exploring the Potential of Web Based Information of Business Popularity for Supporting Sustainable Traffic Management

Abstract This paper explores the potential of using crowdsourcing tools, namely Google “Popular times” (GPT) as an alternative source of information to predict traffic-related impacts. Using linear regression models, we examined the relationships between GPT and traffic volumes, travel times, pollutant emissions and noise of different areas in different periods. Different data sets were collected: i) crowdsourcing information from Google Maps; ii) traffic dynamics with the use of a probe car equipped with a Global Navigation Satellite System data logger; and iii) traffic volumes. The emissions estimation was based on the Vehicle Specific Power methodology, while noise estimations were conducted with the use of “The Common Noise Assessment Methods in Europe” (CNOSSOS-EU) model. This study shows encouraging results, as it was possible to establish clear relationships between GPT and traffic and environmental performance.

Fast and robust map‐matching algorithm based on a global measure and dynamic programming for sparse probe data

The location data from positioning devices such as those utilising global navigation satellite system (GNSS) provides vital information for the probe-car systems aiming at solving road-traffic problems. In the case of the Japanese Electronic Toll Collection System 2.0, a huge amount of probe data can be gathered at intervals of 200 m throughout the country. However, it is not easy for conventional map-matching algorithms to perform appropriately when they target this sparse probe data. Since for the sparser probe data of this range, it is required to check the reachability of the probe car between adjacent position fixes by using the Dijkstra's algorithm or A* algorithms. These algorithms, however, consume much computation power and can be a serious obstacle for map-matching processing, especially in real-time applications. The authors propose a new dynamic-programming-based map-matching algorithm, which can also reduce the calculation time for the reachability test by introducing a hash algorithm. The results of the evaluation confirm the robustness and the effectiveness of the proposed algorithm in terms of both accuracy and computational performance.

Impact on Network Performance of Probe Vehicle Data Usage: An Experimental Design for Simulation Assessment

Probe-based technologies are proliferating as a means of inferring traffic states. Technological companies are interested in traffic data for computing the best routes in a traffic-aware manner and they also provide real-time traffic information with certain temporal accuracy. This paper analyses and evaluates how data provided by a fleet of probe cars can be used to develop a navigation service and how the penetration rate of this service affects a set of city-scale KPIs (Key Performance Indicators) and driver KPIs. The case study adopts a model-driven approach in which microscopic simulation emulates real-size fleets of probe vehicles that provide positions and speed data. What is noteworthy about the modelling behaviour is that drivers are segmented according to their knowledge of network conditions for selected trips: experts, regular drivers, and tourists. The paper presents and discusses the modelling approach and the results obtained from an experimental Barcelona CBD model designed to evaluate the penetration rates of probe vehicles and route guidance. An analysis of the simulation experiments reveals remarkable links among city-scale KPIs, which—from a multivariate point of view—is a novelty. A simulation-based framework for results analysis and visualization is also introduced in order to simplify the simulation results analysis and easily visualize OD paths for driver segments.

An Evaluation of Information Sharing Parking Guidance Policies Using a Bayesian Approach

Real-time parking occupancy information is critical for a parking management system to facilitate drivers to park more efficiently. Recent advances in connected and automated vehicle technologies enable sensor-equipped cars (probe cars) to detect and broadcast available parking spaces when driving through parking lots. In this paper, we evaluate the impact of market penetration of probe cars on the system performance, and investigate different parking guidance policies to improve the data acquisition process. We adopt a simulation-based approach to impose four policies on an offstreet parking lot influencing the behavior of probe cars to park in assigned parking spaces. This in turn effects the scanning route and the parking space occupancy estimations. The last policy we propose is a near-optimal guidance strategy that maximizes the information gain of posteriors. The results suggest that an efficient information gathering policy can compensate for low penetration of connected and automated vehicles. We also highlight the policy trade-off that occur while attempting to maximize information gain through explorations and improve assignment accuracy through exploitations. Our results can assist urban policy makers in designing and managing smart parking systems.

A Principal Curve-based method for Geospatial Data Smoothing

GIScience 2016 Short Paper Proceedings A Principal Curve-based method for Geospatial Data Smoothing Xiliang Liu, Feng Lu, Kang Liu, Peiyuan Qiu, Li Yu, Mingxiao Li State Key Lab of Resources and Environmental Information system, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences {liuxl;luf;liukang;yul;limx}@lreis.ac.cn Abstract We propose a principal curve-based method for geospatial data smoothing. Firstly we test its performance with traditional approaches using floating car data (FCD). Secondly we evaluate its robustness with spatial-temporal dependence using Spearman rank correlation analysis. Final results show that the proposed method not only takes precedence over traditional methods (Mean and Median) in accuracy (about 10%-15% higher in RMSE), but also performs more robust, showing a distinct changing trend of the original data. These findings demonstrate the feasibility of the principal curve-based method in geospatial data smoothing. Keywords: Principal curves, Data smoothing, Robustness 1. Introduction Nowadays various GPS-equipped sensors, such as operating vehicles (taxicabs, probe cars, buses, private cars, etc.), mobile phones, wearable devices and so on, have become the mainstream in the research of GIScience and many other location based services (LBS) because of the cost-effectiveness and flexibility compared with other data sources. However, these geospatial data collected from GPS devices cannot be utilized directly owing to: (1) the sampling interval in most cities is low-frequency due to transmission bandwidth, energy consumption and storage pressures, and (2) the spatial-temporal distribution of these GPS- equipped devices among a city or a given region is heterogeneous. To further mine these data, the data sparseness, data missing and noise problem make geospatial data smoothing an unavoidable step. Previous studies mainly focus on parametric approaches including Kalman filter, particle filter, piecewise linear (PWL) curves, and so on. These methods can effectively deal with data noise problem, but behave unsatisfactory with data sparseness and data missing problems. Traditional Mean and Median are also conducted by using current and near-past records from a historical perspective. However, the prerequisite of Gaussian distribution in most cases cannot be satisfied so that the traditional Mean and Median methods can only be employed in linear systems. In this paper, we propose a principal curve-based method in geographical data interpolation. We evaluate its performance using floating car data (FCD), and analyze its robustness with Spearman's rank correlation analysis. A series of experiments demonstrate its feasibility for geospatial data smoothing. 2. Methodology Principal curves give a summarization of the data in terms of a 1-d space nonlinearly embedded in the data space (Hastie and Stuetzle 1989). The original definition of a principal curve f ( t ) ( f 1 ( t ),..., f d ( t )) relies on the self-consistency property of principal components,

Prediction of Fuel Consumption by Analyzing Probe Car Data

Prediction of Fuel Consumption by Analyzing Probe Car Data

Toward Crowdsourcing-Based Road Pavement Monitoring by Mobile Sensing Technologies

In crowdsourcing applications, the quality of the crowdsourced data is decisive to the success of subsequent system-level mining processes. We proposed a smartphone probe car (SPC) system to monitor road pavement. An SPC is essentially an ordinary vehicle with a mounted smartphone that runs sensing programs to objectively assess bumping caused by road anomalies such as potholes and bumps. The proposed system has several features. First, to allow dynamic forming of SPCs, we develop a signal processing heuristic for the extraction of the vertical acceleration components from the accelerometer readings (upon which bumping detection and road surface anomaly assessment rely). By these means, the proposed system provides a driver-friendly environment, requiring neither complicated installation nor driver-assisted training processes, and thus is possible to achieve hassle-free mass deployment such that drivers would be willing to participate in crowdsourcing. Second, based on the underdamped oscillation model, we propose a road anomaly indexing heuristic that is representable for road anomalies rather than vehicle conditions. This will later facilitate the system-level data mining processes in the servers. Third, a prototype SPC system was implemented and extensive field tests were undertaken to verify the performance of our system framework. Furthermore, we experimentally adopted a DENCLUE-like algorithm to mine road anomaly information from reported events to demonstrate any potential benefit from future investigation of data mining process at the system level. We believe the research works introduced in this paper consist the first step toward building an “ecosystem” of SPC-based crowdsourcing traffic and road monitoring applications.

Dynamic Estimation of Headway Distance in Vehicle Platoon System under Unexpected Car-following Situations

The headway distance between vehicles in a platoon is difficult to measure unless the vehicles are equipped with costly equipment such as laser or radar sensors, or an image processing system. This paper proposes an alternate approach to estimating headway distance indirectly from measurable variables such as the acceleration rate and velocity of selected vehicles in the platoon. Assuming a six-vehicle platoon, a particle filter (PF) and an unscented Kalman filter (UKF) are applied in order to estimate the headway of each vehicle based on the measurement variables of three (or all) vehicles in the platoon. The state-space models of the PF and the UKF are given by the conservation equation of headway and the conventional car-following model. To evaluate the PF and UKF performance, two scenarios were prepared: one assumed that prior knowledge of a model parameter differed from what was actually observed as posterior information, the other was a situation where a platoon vehicle slowed down unexpectedly during the car-following process. In both situations, the state-space model itself was unable to describe the dynamics of headway distance precisely, and the PF and the UKF were applied to minimize the headway distance errors caused by the incorrect model parameter and the unexpected vehicle slowdown. Numerical analysis demonstrated that both the PF and UKF were successful in estimating the headway distance, even when the car-following model did not express the true car-following phenomena. We also determined that, if all vehicles are equipped as probe cars, and thus capable of measuring the acceleration rate and velocity, the PF is superior to UKF when estimating the headway distance precisely. However, UKF is more stable than the PF when measurements are not taken from all vehicles, especially when a vehicle in the platoon unexpectedly slows downs during the car-following process.

Determination of GHG Emissions, Fuel Consumption and Thermal Efficiency for Real World Urban Driving using a SI Probe Car

A SI probe car, defined here as a normal commercial car equipped with GPS, in-vehicle FTIR tailpipe emission measurement and real time fuel consumption measurement systems, and temperature measurements, was used for measuring greenhouse gas emissions including CO2, N2O and CH4 under real world urban driving conditions. The vehicle used was a EURO4 emission compliant SI car. Two real world driving cycles/routes were designed and employed for the tests, which were located in a densely populated area and a busy major road representing a typical urban road network. Eight trips were conducted at morning rush hours, day time non-peak traffic periods and evening off peak time respectively. The aim is to investigate the impacts of traffic conditions such as road congestion, grade and turnings on fuel consumption, engine thermal efficiency and emissions. The time aligned vehicle moving parameters with fuel consumption and emission data enabled the micro-analysis of the correlations between these parameters. The trip average data for fuel consumption, thermal efficiency and emissions etc were calculated. The impact of vehicle's transient movements and traffic events on emissions were studied. VSP (Vehicle Specific Power) has been used for the determination of engine power output. The results show that the transient emissions were dominated by sharp accelerations occurred at traffic light and pedestrian crossing. The trip average CO2 and CH4 emissions had a good linear correlation with vehicle's average speed which is directly related to vehicle idle periods but no clear correlation between vehicle's speed and N2O emissions.

RELATIONSHIP OF PEOPLE AND INFRASTRUCTURE DURING THE 3.11 EARTHQUAKE WITH INFORMATION TECHNOLOGY AS MEDIATING CHANNEL

The Great East Japan Earthquake of March 11, 2011 resulted in not only 19,000 deaths and missing persons, mainly in the Tohoku Region, but also the evacuation of more than 400,000 people during the peak period. Although such disasters are very rare, creating robust civil engineering structures to prevent potential threats would be a difficult task. On the other hand, even though information and communication technologies are just one of the means available, they have significant potential in terms of organically connecting support from people in a variety of capacities when an emergency arises. From this perspective, keywords such as “evacuation training,” “group relocation,” “real-time tsunami sensing,” and “soft disaster preventions” emerged as a result of this earthquake. I would like to describe how people utilize information and communication technologies as a mediating channel to secure their safety and security during earthquakes, whether autonomously or at other times through the protection provided by infrastructure facilities. In particular, the emergency mapping technology involving manual preparation as well as input from probe cars is introduced in Section 2 as a successful example of how information technology could serve as a mediating channel. Furthermore, in Section 3, I will describe the activities for the Open Government that did not necessarily function as a mediating channel but remain major issues for the future. In Section 4, the mobile phone technology is discussed in order to consider how mediating channels can be provided on a real-time basis, with consideration for the future.

VANET과 기간통신망간 게이트웨이를 사용한 교통정보 서비스 시스템

We propose a traffic information service system for which traffic data are transmitted over vehicle ad-hoc network from neighbor vehicles, and eventually provided to the traffic information center through a few gateway vehicles which are capable of inter-working with the infrastructure network like 3G. The center broadcasts the traffic information through the FM radio. The proposed scheme allows collecting and analyzing traffic status of large areas without incorporating separated monitoring systems, e.g., probe cars and enables to provide accurate traffic information to drivers in timely manner. We also evaluate its performance by ns-3 simulation.

Potential of Plug-in Hybrid Vehicle to Reduce CO2 Emission Estimated from Probe Car Data in Japan

The reduction of CO2 emission by the transport sector is necessary to be realized the low carbon society. In the near future, further CO2 emission reduction is expected by the diffusion of PHEV. The aim of this study was to evaluate the potential of PHEV to reduce CO2 emission based on real-world driving data (probe car data) and simulation. The probe car data of 35 conventional HEVs from April to August in 2011 were analyzed. The type of simulated PHEV system was all electric range, which operated only by battery power as long as available battery capacity was remaining (EV mode) , and then operated like conventional HEV after battery was depleted (HEV mode). Charging frequency was once a day at home after midnight as a realistic scenario. The results showed that the travel distance of 43% was converted to EV mode, and the gasoline consumption was reduced by 44%. The CO2 emission was totally reduced by 17% considering electric power consumption. CO2 emissions of each vehicle were reduced by 1-44%. CO2 reduction amount of each vehicle varied widely reflecting their each own ways of car use and operating conditions. It is indicated that the diffusion of PHEV is a realistic and efficient measure to reduce CO2 emissions in consideration of actual car use and operating conditions. Furthermore, low carbon power supply as well as diffusion of PHEV is more effective to CO2 reduction.

A Traffic Information Prediction System Based on Global Position System-Equipped Probe Car Reporting

A Traffic Information Prediction System Based on Global Position System-Equipped Probe Car Reporting

VANET기반 교통정보 서비스 방식 성능분석

Abstract We propose a traffic information service for which traffic data are collected over ad-hoc networks from neighbor vehicles, processed to minimize the data size, and eventually provided to its destination. The proposed scheme simply relies on the existing navigtion systems in vehicles and wireless communication devices for vehicle-to-vehicle communication, rather than on a separately established server. It allows collecting and analyzing traffic status of large areas without incorporating separated monitoring systems, e.g., probe cars and enables to provide accurate traffic information to drivers in timely manner. We also evaluate its performance by ns-3 simulation. Key Words : Traffic Information Service, Inter-Vehicle Communication, Simulation * 정회원, 충북도립대학 전자정보계열 전자통신전공접수일자 2012년 5월 12일, 수정완료 2012년 6월 3일게재확정일자 2012년 6월 8일Received: 12 May 2012 / Revised: 3 June 2012 /Accepted: 8 June 2012 ** Corresponding Author: won@cpu.ac.krDept. of Electronic Communication, Chungbuk Prov. University, Korea

Development of Calculating Method of Travel Time Reduction Benefit When Using CNS: By Using Taxi Probe Car Data of UTIS

Development of Calculating Method of Travel Time Reduction Benefit When Using CNS: By Using Taxi Probe Car Data of UTIS

A Real-Time Traffic Information Model Using GPS-Based Probe Car Data

In recent year, the rise of economic growth and technology advance leads to improve the quality of service of traditional transport system. Intelligent Transportation System (ITS) has become more and more popular. At present, the collection of real-time traffic information is executed in two ways: (1) Stationary Vehicle Detectors (VD) and (2) Global Position System (GPS)-based probe cars reporting. However, VD devices need a large sum of money to build and maintain. Therefore, we propose the linear regression model to infer the equation between vehicle speed and traffic flow. The traffic flow can be estimated from the speed which is obtained from GPS-based probe cars. In experiments, the Speed Error Ratio (SER) and Flow Error Ratio (FER) of linear regression model are 4.60% and 24.63% respectively. The estimated speed and traffic flow by using linear regression model is better than by using linear model, power law model, exponential model, and normal distribution model. Therefore, the linear regression model can be used to estimate traffic information for ITS.

Examination of the Applicability of Probe Car Data to Assessment of the Effects of Road-Safety Projects

To achieve “the safest roads in the world”, the government of Japan has been addressing projects aimed at reducing annual traffic fatalities to below 5,000 by 2012, working hand-in-hand with the private sector. In this study, the potential for application of probe car data in quantitative assessment of project impacts is investigated after collecting such data from taxis and lorries. It is demonstrated that the changes in vehicle behaviors leading to accidents are indicated quantitatively at high-accident locations through the before-and-after projects. Probe car data therefore have the potential for application in measuring the effects of road safety projects.