Floating car data

About: Floating car data is a research topic. Over the lifetime, 7541 publications have been published within this topic receiving 110971 citations.

Apparatus and method for processing and displaying traffic information in an automotive navigation system

Abstract: A vehicle navigation system for use with a traffic information broadcast system which transmits traffic data relating to primary road segments and a communication system which transmits traffic data relating to road segments in a local geographic area. A broadcast receiver receives the broadcast traffic data from the broadcast system and stores the received data. A communication transmitter, when activated, transmits a data request to the communication system. A communication receiver receives the local traffic data from the communication system in response to the data request while a processor displays traffic information on a display screen corresponding to the received broadcast traffic data and the received local traffic data. The local traffic data includes both traffic information relating to local road segments as well as non-traffic temporal events, such as weather, and time-related events which affect traffic flow. A database of historic traffic flow for the road segments is periodically updated to reflect new traffic data.

Real time vehicle guidance and forecasting system under traffic jam conditions

Abstract: A system and method for real time vehicle guidance by Central Traffic Unit are presented. The proposed vehicle Guidance System includes a plurality of vehicles equipped with Individual Mobile Units including GPS units (position determining systems adapted to determine their present position) and communicatively linked to the Central Traffic Unit computer server. The Central Traffic Unit broadcasts the updated traffic patterns in real time thereby enabling the Individual Mobile Units to dynamically calculate the desired optimal travel paths. In response to a request from a driver for a route update from his present position to a desired destination, the Individual Mobile Unit searches for an optimal (usually fastest) route and shows it to the driver. In route searching by the minimal time criterion, the Individual Mobile Unit relies on estimated travel times stored in its database, and may also use current real time information on bottleneck situations received from Central Traffic Unit. The forecasting system allows the driver to enter alternative time schedules for the same destination and receive alternative travel time estimates for the same destination depending on the estimated traffic volumes on the roads at that particular time. The backbone of the system is a group of Sample Mobile Units equipped with RF transmitters that communicate their present position to the Central Traffic Unit at predetermined time intervals. The Central Traffic Unit uses those sample vehicles as antennas by tracking their positions for creating and maintaining a network of real time traffic load disposition in various geographical areas. To be able to detect a bottleneck situation when it arises and to estimate a current travel time for a corresponding section of road, the Central Traffic Unit maintains a list of sample vehicles that recently exited that section. If the times those vehicles have spent on the section differ considerably from a regular time stored in the database, Central Traffic Unit uses statistical tools for forecasting the future travel time along this section. Simultaneously, the Central Traffic Unit broadcasts updated travel times and any new information on current traffic jams and slow-down bottleneck situations in a given geographical location.

Self-Control of Traffic Lights and Vehicle Flows in Urban Road Networks

Abstract: Based on fluid-dynamic and many-particle (car-following) simulations of traffic flows in (urban) networks, we study the problem of coordinating incompatible traffic flows at intersections. Inspired by the observation of self-organized oscillations of pedestrian flows at bottlenecks [D. Helbing and P. Moln\'ar, Phys. Eev. E 51 (1995) 4282--4286], we propose a self-organization approach to traffic light control. The problem can be treated as multi-agent problem with interactions between vehicles and traffic lights. Specifically, our approach assumes a priority-based control of traffic lights by the vehicle flows themselves, taking into account short-sighted anticipation of vehicle flows and platoons. The considered local interactions lead to emergent coordination patterns such as ``green waves'' and achieve an efficient, decentralized traffic light control. While the proposed self-control adapts flexibly to local flow conditions and often leads to non-cyclical switching patterns with changing service sequences of different traffic flows, an almost periodic service may evolve under certain conditions and suggests the existence of a spontaneous synchronization of traffic lights despite the varying delays due to variable vehicle queues and travel times. The self-organized traffic light control is based on an optimization and a stabilization rule, each of which performs poorly at high utilizations of the road network, while their proper combination reaches a superior performance. The result is a considerable reduction not only in the average travel times, but also of their variation. Similar control approaches could be applied to the coordination of logistic and production processes.

Visual Traffic Jam Analysis Based on Trajectory Data

Abstract: In this work, we present an interactive system for visual analysis of urban traffic congestion based on GPS trajectories. For these trajectories we develop strategies to extract and derive traffic jam information. After cleaning the trajectories, they are matched to a road network. Subsequently, traffic speed on each road segment is computed and traffic jam events are automatically detected. Spatially and temporally related events are concatenated in, so-called, traffic jam propagation graphs. These graphs form a high-level description of a traffic jam and its propagation in time and space. Our system provides multiple views for visually exploring and analyzing the traffic condition of a large city as a whole, on the level of propagation graphs, and on road segment level. Case studies with 24 days of taxi GPS trajectories collected in Beijing demonstrate the effectiveness of our system.