The present invention relates to systems and methods for estimating traffic signal information. Traffic signals have been an indispensable element of our transportation networks since their inception and are not likely to change form or function in the foreseeable future. While traffic signals ensure safety of conflicting movements at intersections, they also cause much delay, wasted fuel, and tailpipe emissions. Frequent stops and goes induced by a series of traffic lights often frustrate drivers. In arterial driving, the complex and unknown switching pattern of traffic signals often makes accurate travel time estimation or optimal routing impossible, even with modern traffic-aware in-vehicle navigation systems.
Many of these difficulties arise due to the lack of information about the current and future states of traffic signals. In an ideal situation where the state of a signal's timing and phasing is known, the speed of a vehicle could be adjusted for a timely arrival at green. One can expect considerable fuel savings in city driving with related art predictive cruise control algorithms. When idling at red becomes unavoidable, knowledge of the remaining red time can determine if an engine shut-down is worthwhile. A collision warning system can benefit from the signal timing information and warn against potential signal violations. Future navigation systems that have access to the timing plan of traffic lights can find arterial routes with less idling delay and can also provide more accurate estimates of trip time.
The main technical challenge to deploying such in-vehicle functionalities is in the reliable estimation and prediction of Signal Phase and Timing (SPaT) information. Uncertainties arising from the clock drift of fixed-time signals, various timing plans of actuated traffic signals, and traffic queues render this a challenging and open-ended problem. Direct access to signal timing plans and the real-time state of the signal is prohibitively difficult, due to the hundreds of local and federal entities that manage the more than 300,000 traffic lights across the United States alone. Even when such access is granted, much effort and time must be spent in structuring information from various municipalities in standard and uniform formats. The recent emphasis on Dedicated Short Range Communication (DSRC) technology for communicating the state of traffic signals to nearby vehicles has safety benefits, but requires heavy infrastructure investments and is limited by its short communication range.
In recent years several related art methods have been developed that utilize mobile phone or vehicle probe data for estimation of traffic flow. Today many traffic information providers, such as Google®, INRIX®, and Waze®, use data from vehicle and cellular phone probes, as well as other devices, to estimate the severity of traffic on highways nearly in real-time. However, such algorithms perform relatively poorly in arterial networks, because traffic signals induce complex queue and stop-and-go dynamics. Other related art methods have focused on estimating queue lengths and determining locations of traffic signals and stop signs by using vehicle probe data. However, the related art does not provide a systematic attempt to derive SPaT information from available vehicle data streams. M. Kerper, C. Wewetzer, A. Sasse, and M. Mauve, “Learning traffic light phase schedules from velocity profiles in the cloud,” in Proceedings of 5th International Conference on New Technologies, Mobility and Security (NTMS), 2012, pp. 1-5, describes a simulation study that is performed to show the feasibility of determining SPaT information using probe data. However, the results are limited by the assumptions that the data is updated at a high rate of approximately 1 Hz, and that the penetration level is high.
Unfortunately, currently one cannot expect high update rates from public fleets that broadcast their information, nor is there a proliferation of vehicle probes. Most related art vehicle probes provide only event-based updates, for example at a time of a crash or an air-bag deployment. Some data sources, such as San Francisco taxi cab data available through the Cabspotting program, have update rates of only once per minute. Slightly more frequent updates are available through NextBus®, a service that provides a real-time eXtensible Markup Language (XML) feed of a global positioning system (GPS) time stamp, position, velocity, and several other attributes of transit buses of a few cities in North America. Some instances of this feed, such as San Francisco MUNI stream, have update rates on the order of twice per minute. Further, intersections along a bus route are generally traversed by a bus every few minutes during the day.
Accordingly, it would be advantageous to provide systems and methods for estimating traffic signal phase and timing information from statistical patterns in low-frequency probe data. For example, it would be advantageous to estimate cycle times and durations of reds and greens for fixed-time traffic lights, and to estimate the future starts of greens in real-time. Such information about traffic signals' phase and timing may be valuable in enabling new fuel efficiency and safety functionalities in connected vehicles. For example, velocity advisory systems could use the estimated timing plan to calculate velocity trajectories that reduce idling time at red signals and therefore improve fuel efficiency and lower emissions. In addition, advanced engine management strategies could shut down the engine in anticipation of a long idling interval at red. Further, intersection collision avoidance and active safety systems could also benefit from the predictions. Various applications of SPaT information are discussed in copending U.S. application Ser. No. 13/840,830, the entire disclosure of which is incorporated by reference.
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