Modeling of traffic data characteristics by Dirichlet Process Mixtures

Conference Theme: Green Automation Toward a Sustainable SocietyThis paper presents a statistical method for modeling large volume of traffic data by Dirichlet Process Mixtures (DPM). Traffic signals are in general defined by their spatial-temporal characteristics, of which some can be common or similar across a set of signals, while a minority of these signals may have characteristics inconsistent with the majority. These are termed outliers. Outlier detection aims to segment and eliminate them in order to improve signal quality. It is accepted that the problem of outlier detection is non-trivial. As traffic signals generally share a high degree of spatial-temporal similarities within the signal and between different types of traffic signals, traditional modeling approaches are ineffective in distinguishing these similarities and discerning their differences. In regard to modeling the traffic data characteristics by DPM, this paper conveys three contributions. First, a new generic statistical model for traffic data is proposed based on DPM. Second, this model achieves an outlier detection rate of 96.74% based on a database of 764,027 vehicles. Third, the proposed model is scalable to the entire road network. © 2012 IEEE.published_or_final_versio

Privacy-Friendly Mobility Analytics using Aggregate Location Data

Location data can be extremely useful to study commuting patterns and disruptions, as well as to predict real-time traffic volumes. At the same time, however, the fine-grained collection of user locations raises serious privacy concerns, as this can reveal sensitive information about the users, such as, life style, political and religious inclinations, or even identities. In this paper, we study the feasibility of crowd-sourced mobility analytics over aggregate location information: users periodically report their location, using a privacy-preserving aggregation protocol, so that the server can only recover aggregates -- i.e., how many, but not which, users are in a region at a given time. We experiment with real-world mobility datasets obtained from the Transport For London authority and the San Francisco Cabs network, and present a novel methodology based on time series modeling that is geared to forecast traffic volumes in regions of interest and to detect mobility anomalies in them. In the presence of anomalies, we also make enhanced traffic volume predictions by feeding our model with additional information from correlated regions. Finally, we present and evaluate a mobile app prototype, called Mobility Data Donors (MDD), in terms of computation, communication, and energy overhead, demonstrating the real-world deployability of our techniques.Comment: Published at ACM SIGSPATIAL 201

Modeling Lane-Changing Behavior in Freeway Off-Ramp Areas from the Shanghai Naturalistic Driving Study

The objective of this study is to investigate lane-changing characteristics in freeway off-ramp areas using Shanghai Naturalistic Driving Study (SH-NDS) data, considering a four-lane freeway stretch in various traffic conditions. In SH-NDS, the behavior of drivers is observed unobtrusively in a natural setting for a long period of time. We identified 433 lane-changing events with valid time series data from the whole dataset. Based on the logit model developed to analyze the choice of target lanes, a likelihood analysis of lane-changing behavior was graphed with respect to three traffic conditions: free flow, medium flow, and heavy flow. The results suggested that lane-changing behavior of exiting vehicles is the consequence of the balance between route plan (mandatory incentive) and expectation to improve driving condition (discretionary incentive). In higher traffic density, the latter seems to play a significant role. Furthermore, we found that lane-change from the slow lane to the fast lane would lead to higher speed variance value, which indicates a higher crash risk. The findings contribute to a better understanding on drivers’ natural driving behavior in freeway off-ramp areas and can provide important insight into road network design and safety management strategies

SEE-TREND: SEcurE Traffic-Related EveNt Detection in Smart Communities

It has been widely recognized that one of the critical services provided by Smart Cities and Smart Communities is Smart Mobility. This paper lays the theoretical foundations of SEE-TREND, a system for Secure Early Traffic-Related EveNt Detection in Smart Cities and Smart Communities. SEE-TREND promotes Smart Mobility by implementing an anonymous, probabilistic collection of traffic-related data from passing vehicles. The collected data are then aggregated and used by its inference engine to build beliefs about the state of the traffic, to detect traffic trends, and to disseminate relevant traffic-related information along the roadway to help the driving public make informed decisions about their travel plans, thereby preventing congestion altogether or mitigating its nefarious effects

AN APPLICATION OF FUZZY LOGIC IN URBAN TRAFFIC INCIDENT DETECTION

Traffic congestion in urban areas is an increasing problem around the world. Traffic incidents (such as accidents) are considered as the major source of traffic congestion. Traffic incidents have negative impacts on traffic flow, air pollution and fuel consumption. As a result, increasing interest in finding new techniques to deal with this issue has been shown. Traffic incident-management systems can decrease the effect of such events and keep roads capacity as close as possible to normal levels. Incident detection system is an important part of any incident management system. This thesis proposes a new approach to incident detection in urban traffic networks using fuzzy logic theory with the objective of reducing traffic delays and increasing road safety. The proposed detection system can be then integrated with a traffic incident management system to reduce traffic congestion related to non-recurrent incident situations. A methodology has been established based on fuzzy logic for detecting incident status in urban areas using detector accumulative count differences. Three fuzzy models were developed and evaluated using simulated data (generated using the commercial software: PTV VISSIM by PTV Group). The fuzzy models can detect incident status on a regular basis (every minute). Performance measures were introduced to capture the capabilities of the suggested models in detecting incidents. The dissertation concludes with a summary of the major findings, recommendations and future research

Geometric models for video surveillance in road environments: vehicle tailgating detection

Traffic accidents constitute one of the main causes of death in many countries. Despite the current efforts devoted to mitigate the effects of road incidents, there are still some variables affecting this problem which are not yet under control or regulation. Spain, for instance, still lacks official regulations about especially risky driving behaviours, such as tailgating. In many cases, the rationale behind is that these behaviours are hard or expensive to detect reliably, thus limiting the extent of the automatic detection systems. This paper proposes a method to identify certain elements in road scenarios, define geometric models that allow computing quantitative measures of the scene and, consequently, detect offending driving behaviours. In this work, we have focused on the particular case of study of tailgating detection. However, the proposed geometric models might become the basis of many other useful applications.Ingeniería de Sistemas Audiovisuale

Detection and Classification of Traffic Anomalies using Microscopic Traffic Variables

Accepted versio

Discovering critical traffic anomalies from GPS trajectories for urban traffic dynamics understanding

Traffic anomaly (e.g., traffic jams) detection is essential for the development of intelligent transportation systems in smart cities. In particular, detecting critical traffic anomalies (e.g., rare traffic anomalies, sudden accidents) are far more meaningful than detecting general traffic anomalies and more helpful to understand urban traffic dynamics. For example, emerging traffic jams are more significant than regular traffic jams caused by common road bottlenecks like traffic lights or toll road entrances;  and discovering the original location of traffic chaos in an area is more important than finding roads that are just congested. However, using existing traffic indicators that represent traffic conditions, such as traffic flows and speeds, for critical traffic anomaly detection may be not accurate enough. That is, they usually miss some traffic anomalies while wrongly identifying a normal traffic status as an anomaly. Moreover, most existing detection methods only detect general traffic anomalies but not critical traffic anomalies. In this thesis, we provide two new indicators: frequency of jams (captured by stop-point clusters) and Visible Outlier Indexes (VOIs) (based on the Kolmogorov-Smirnov test of speed) to capture critical traffic anomalies more accurately. The advantage of our proposed indicators is that they help separate critical traffic anomalies from general traffic anomalies. The former can discover rare anomalies with low frequency, and the latter can find unexpected anomalies (i.e., when the difference between the predicted VOI and the real VOI is great). Based on these two indicators, we provide three novel methods for comprehensive traffic anomaly analysis, including traffic anomaly identification, prediction, and root cause discovery. First, we provide a novel analysis of spatial-temporal jam frequencies (ASTJF) method for identifying rare traffic anomalies. In the ASTJF method, spatially close stop-points in a time bin are grouped into stop-point clusters (SPCs) using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm; an SPC is an instance of a spatiotemporal jam. Then, we develop a new adapted Hausdorff distance to measure the similarity of two SPCs and put SPCs which are relevant to the same spatiotemporal jam into a group. Finally, we calculate the number of SPCs in a group as the frequency of the corresponding traffic jams; traffic anomalies are classified as regular jams with high frequency and emerging jams with low frequency. The ASTJF method can correctly identify critical traffic anomalies (i.e., emerging jams). Second, we propose a novel prediction approach -  Visible Outlier Indices and Meshed Spatiotemporal Neighborhoods (VOIMSN) method. In this method, the trajectory data from the given region's geographic spatial neighbors and its time-series neighbors are both converted to the abnormal scores measured by VOIs and quantified by the matrix grid as the input of the prediction model to improve the accuracy. This method provides a comprehensive analysis using all relevant data for building a reliable prediction model. In particular, the proposed meshed spatiotemporal neighborhoods with arbitrary shape, which comprises all potential anomalies instead of just past anomalies, is theoretically more accurate than a fixed-size neighborhood for anomaly prediction. Third, we provide an innovative and integrated root cause analysis method using VOI as the probabilistic indicator of traffic anomalies. This method proposes automatically learns spatiotemporal causal relationships from historical data to build an uneven diffusion model for detecting the root cause of anomalies (i.e., the origin of traffic chaos). It is demonstrated to be better than the heat diffusion model. Experiments conducted on a real-world massive trajectory dataset demonstrate the accuracy and effectiveness of the proposed methods for discovering critical traffic anomalies for a better understanding of urban traffic dynamics