Data processing: vehicles – navigation – and relative location – Vehicle control – guidance – operation – or indication – Traffic analysis or control of surface vehicle
Reexamination Certificate
2001-07-10
2003-03-25
Nguyen, Tan Q. (Department: 3661)
Data processing: vehicles, navigation, and relative location
Vehicle control, guidance, operation, or indication
Traffic analysis or control of surface vehicle
C701S118000, C455S456500, C379S112010
Reexamination Certificate
active
06539300
ABSTRACT:
FIELD OF THE INVENTION
This invention relates generally to traffic control systems. More specifically, the present invention relates to a traffic control system that optimizes traffic flow based on information obtained via a wireless telephone network.
BACKGROUND OF THE INVENTION
Optimization of Traffic Signal Timings in Regional Traffic Control Systems
Problems in traffic control have been studied extensively over the last few decades. Conventional traffic control systems comprise three major components: hardware infrastructure, information gathering systems, and traffic control software including mathematical models and algorithms. At present we are primarily interested in software models and algorithms, and in information gathering systems.
Existing Methods of Gathering Information on Traffic Conditions
Due to ever increasing traffic volume, traffic control and information acquisition have become an important part of the overall traffic management strategy.
Generally, dynamic traffic data are gathered by three methods:
1. Road sensor devices such as induction loops, traffic detectors, and TV cameras mounted on poles;
2. Mobile traffic units such as police, road service, helicopters, weather reporting devices, etc.
3. Mobile positioning and communication systems using GPS devices or similar vehicle-tracking equipment.
The disadvantages of these data collection methods are summarized as follows:
1. Relatively high cost of required capital investment into road devices especially when carried out within existing road infrastructures;
2. Relatively limited number of organizations such as trucking, delivery and other service companies utilizing reporting vehicles equipped with GPS devices;
3. In general only small geographical areas are effectively covered due to specific nature of service tasks, apart from the relatively small number of cars equipped with required GPS devices necessary for precise position determination.
In a recent development, GPS reporting devices have been mounted on individual cars to provide positioning information of vehicles via wireless mobile communication systems. The additional expenditures required by these mobile systems are much lower than by the traditional methods using fixed road metering. One disadvantage of these systems is in the relatively limited number of cars equipped with required GPS devices necessary for precise position determination, and therefore relatively small geographical areas that can be effectively covered.
Modes of Operation of Traffic Control Systems
As originally coined, the term “traffic control” implied a human operator, i.e. a policeman, or a specially trained dispatcher who directed traffic flows across road intersections. This “controller” used his experience and intuition to evaluate traffic loads and waiting times in various directions and lanes, and for changing phase timing accordingly.
Following the introduction of electric traffic signals at the beginning of the twentieth century, progress in the methods of traffic control closely followed that of the control technology, and subsequently the progress of computer science.
Initially, simple electric clocks allocated a specific amount of time to each phase in a specific pattern to controlled traffic signals. These early clock systems were preset and provided no adjustment for peak traffic periods, or for unusual conditions.
The next step was to create a clock that operated differently at different times of the day, and used several different control patterns for different times of day. Those patterns were determined from historical data.
Starting in the mid-1960's, computers were increasingly utilized in traffic control. These computers made it possible to create actuated controllers that had the ability to adjust the signal phase lengths in response to traffic flow in real time. If no vehicles were detected on an approach to an intersection, the controller could skip that phase or reduce the phase to a fixed minimum time. Thus, the green time for each approach was a function of the traffic flow, and could be varied between minimum and maximum lengths depending on traffic flows.
Modes of operation of modern traffic control systems can be divided into three primary categories: 1) pre-timed; 2) actuated (including both semi-actuated and fully actuated); and 3) traffic responsive. Under pre-timed operation, the master controller sets signal phases and cycle lengths on predetermined rates based on historical data.
An actuated controller operates based on traffic demands as registered by the actuation of vehicle and/or pedestrian detectors. There are several types of actuated controllers, but their main feature is the ability to adjust the phases in response to traffic flow.
A semi actuated controller maintains green on the major street except when vehicles are registered on minor streets, and then always return the right of way to the major street.
A fully actuated controller measures traffic flow on all approaches and makes assignments of the right of way in accordance with traffic demands. As such, a fully actuated controller requires placement of detectors on all approaches to the intersection. Thereby increasing installation and maintenance costs considerably.
In the traffic responsive mode, the system responds to inputs from traffic detectors and may react in one of the following ways:
Use vehicle volume data as measured by traffic detectors;
Perform pattern matching—the volume and occupancy data from system detectors are compared with profiles in memory, and the most closely matching profile is used for decision making;
Perform future traffic prediction—projections of future conditions are computed based on data from traffic detectors.
Control Algorithms for Optimization of Timings for Traffic Signals
A number of algorithms exist that purport to optimize performance of traffic responsive controllers that make use of various techniques such as linear programming, dynamic programming, fuzzy logic, regression analysis, and optimization and prediction procedures. The objective function that is usually set up to be optimized is some measure of overall traffic delay at an intersection or at a number of intersections, while the major control parameter for achieving this is the distribution of green and red light timings among different phases.
The usual framework for those algorithms is as follows. Signal timings should reflect the number of vehicles present on each approach to an intersection and the pattern of arrivals in the near future. The current queue lengths on each approach are identified by locating slow-moving and stationary traffic close to the stop-line. Algorithms minimize the total delay subject to certain constraints. Such constraints are:
1. Adequate capacity for all allowed traffic movements; and
2. Safety constraints (minimum number of seconds for green and inter-green times).
Minimization is performed over the pre-selected planning time horizon, which limits the forward time interval for which computations are made. As optimization is performed continuously, we have a rolling horizon framework.
The rate of delay on an approach is estimated as proportional to the number of vehicles in the queue. Accordingly, the total rate of delay at the intersection is the sum over all streams of these rates of delay. The objective function for optimization is the sum of those total rates of delay over the planning time period, which represents the total delay incurred. A slightly different formulation of the objective of optimization is minimization of the weighted sum of the estimated rate of delay and the number of stops per unit time for all traffic streams. In such a formulation the problem is amenable to treatment by mathematical optimization methods. In particular, by dynamic programming and linear programming techniques.
Most conventional attempts for real time responsive control are either optimized on a per intersection basis or make highly restrictive simplifying assumptions in treating multiple intersection problems. Still, there are a few works t
Makor Issues and Rights Ltd.
Nguyen Tan Q.
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