Predicting traffic conditions for road segments is the prelude of working on intelligent transportation. Many existing methods can be used for short-term or long-term traffic prediction, but they focus more on regions than on road segments. The lack of fine-grained traffic predicting approach hinders the development of ITS. Therefore, MapLSTM, a spatio-temporal long short-term memory network preluded by map-matching, is proposed in this paper to predict fine-grained traffic conditions. MapLSTM first obtains the historical and real-time traffic conditions of road segments via map-matching. Then LSTM is used to predict the conditions of the corresponding road segments in the future. Breaking the single-index forecasting, MapLSTM can predict the vehicle speed, traffic volume, and the travel time in different directions of road segments simultaneously. Experiments confirmed MapLSTM can not only achieve prediction for road segments based a large scale of GPS trajectories effectively but also have higher predicting accuracy than GPR and ConvLSTM. Moreover, we demonstrate that MapLSTM can serve various applications in a lightweight way, such as cognizing driving preferences, learning navigation, and inferring traffic emissions.
Traffic prediction of road segments is a fundamental issue in the Intelligent Transportation Systems (ITS), which can be hopefully used for planning optimal driving routes [
In general, the power of effectively predicting the future traffic conditions for road segments comes from the historical and real-time traffic information. According to the duration for the future, 3-10 days, 1-3 days, within 1 day, and no more than 15 minutes, traffic flow forecast usually is included long-term, recent-term, short-term and short-time [
Traffic network possesses complicated spatio-temporal relationship. The prediction methods should have accuracy, robustness, adaptability and portability as the traffic flow is a high-dynamic, high-dimensional, non-linear and non-stationary random process. Traffic conditions of road segments are influenced inevitably by the spatio-temporal information in the traffic network. Deep learning can be used to model high-level abstractions by using multiple non-linear transformations, while the learning network has rarely taken the overall spatio-temporal dynamic pattern into account. It is not convincing to achieve accurate traffic prediction merely by spatial relations between regions or road segments. Hence, the prediction results perform not well at certain times, which occur especially when there are insufficient GPS trajectories through road segments. Based on this, it is proper to consider more supplementary aspects such as map-matching technology used to recognize traffic conditions for road segments accurately and finely.
In this paper, we propose a fine-grained and lightweight approach for traffic predicting of road segments, named MapLSTM, a spatio-temporal long short-term memory network (LSTM [
(1) Breaking through the difficulty of obtaining segment-based traffic data, we perform the cognizing of road-grained traffic conditions via map-matching technology.
(2) Based on a large scale of taxi GPS trajectories, we propose MapLSTM to extract features from the high-dynamic, high-dimensional, non-linear and non-stationary traffic flow. And we confirm that MapLSTM have a higher predicting accuracy than GPR [
(3) We demonstrate MapLSTM can serve to various pragmatic applications: cognizing driving preferences, learning navigation and inferring traffic emissions.
The remainder of this paper is organized as follows: Section
Traffic condition prediction can not only be used as the design basis of signal control of ITS but also provide decision support for dynamic route guidance. Whereas, there are still some bottlenecks in short or long term traffic prediction through a lot of real spatio-temporal data.
Spatio-temporal semi-supervised learning model proposed in [
A vehicle speed is influenced by many factors: the vehicle type, the traffic conditions and the driver’s behaviour. A data driven model is proposed in [
DeepSense [
Understanding traffic density from large-scale images is another way to recognize the traffic status. Reference [
In addition, the predicted object is univocal in the existing methods, more is traffic volume or speed, which can merely infer the traffic state of the road segment is congestion, slow, normal, moderate, and unimpeded. It is necessary to explore fine-grained and accurate perception in a simple way.
In this section, we first provide materials on GPS trajectory, map-matching, and LSTM. Then we depict MapLSTM designed for traffic prediction.
Taxis can be considered as ubiquitous mobile sensors constantly probing a city’s rhythm and pulse. Being inherent characteristic, GPS-based taxies have proven to be an extremely useful data source for uncovering the underlying traffic behaviour. So far, the taxi GPS data have been used for urban computing, detecting hot spots, map reconstruction, finding routes, and so on [
The GPS records of a large number of taxis in a city are routinely saved to a log file
An example of GPS log and GPS trajectories.
Map-matching is the process of aligning a sequence of observed GPS positions with the road network on a digital map [
As shown in Figure
Map-matching when before and after.
LSTM [
The calculation of each element of LSTM is shown in Algorithm
MapLSTM is fine-grained and lightweight way. It only requires sampled GPS points of vehicles and not need to deploy expensive traffic sensors in urban and not use the unobtainable data from ground loop. In this section, we describe MapLSTM in detail.
Figure
MapLSTM framework for traffic prediction. It consists of three processes: Map-matching, data processing and LSTM predicting.
MapLSTM enables cognition of road segment-based traffic conditions in a lightweight way. For the real-time cognition of global situations, MapLSTM is still valid by collaboration computing where a groups of cells work together to accomplish a relatively large task. Edge computing after cloud computing is a typical collaborative computing environment and has been widely used [
Before map-matching, it is necessary to have a information understanding about roads and vehicles. Table
An example with a sample of the main information about road and vehicle.
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| ID | MapID | PathName | Pathclass | Oneway | Width | Length | Direction | Meters |
59565200918 | 595652 | Xing Fu Xi Jie | 4 | F | 30 | 0.284 | 2 | 3.68 | |
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| ID | Bearing | Speed | State | Longitude | Latitude | Event | Time | Positioning |
6409 | 84 | 46 | 1 | 3973633 | 11633100 | 1 | 20160916182046 | GPS/BeiDou/Mix |
ST-Matching [
After map-matching, roads information where the vehicles are located can be easily obtained, and the traffic data about the roads can also be clearly gained after statistics in turn.
The raw trajectory data cannot be used directly for our predicting task. It is necessary to match and statistics at first. If we want to get the traffic status prediction of road segments, we need to make a segment-based statistics about the traverse time in different directions, the vehicle speed and the traffic volume.
The data of traverse time in different directions, the average vehicle speed and traffic volume of road segments can be generated by Algorithm
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) Imitate GetData( (24) (25) (26)
We compare the following experiments to verify the performance of MapLSTM.
A large scale of real taxi trajectory data are used in our predicting task. The data package of GPS log includes over 400,000 taxicabs’ trajectories in November 2012, Beijing. And full-scale entries are contained during 24 hours for each day. We use data between 8:00
There are too many segments in road network
Traffic data about road segments at 8:00 on November 1, 2012.
In MapLSTM, the obtained dataset is divided into training set and test set in an
ConvLSTM has the same dataset as MapLSTM, and the model has
Mean absolute error (MAE) is the most commonly used criteria in predictive algorithms and is employed to evaluate the proposed MapLSTM.
As shown in Table
MAEs comparison of GPR, ConvLSTM, and MapLSTM.
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| 70.79 | 46.43 | 66.81 | 70.7 | 63.2 | 66.08 | |
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| Train | 18.78 | 7.18 | 18.75 | 18.27 | 17.77 | 19.01 |
Test | 19.27 | 7.32 | 18.71 | 18.11 | 18.14 | 19.29 | |
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| Train | 19.44 | 7.13 | 18.75 | 18.59 | 17.82 | 19.46 |
Test | 18.91 | 6.89 | 18.71 | 17.96 | 17.85 | 18.93 | |
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It is important to note that MAE is affected by the accuracy of the raw data and it will decline if the dataset is large enough.
Different drivers have different preferences about different types of roads, and they also have different impulse to reroute roads due to their different tolerance about the cost expectations of current congestion. For example, the drivers with low tolerance may choose a highway bypass which have a lower congestion cost expectations but have more traffic lights. Tolerance of drivers changes dynamically with various spatial-temporal conditions such as travel distance, congestion time, and arrival time. Therefore, a large deviation between the traffic optimization results and the actual expectation of drivers will lead to failure of traffic scheduling. Quite a few drivers choose a looked like shortest road, only to find the route is congested by many vehicles whose drivers make a similar decision.
The traditional route planning methods are more inclined to train drivers’ basic selection tendency and do not have personalized features. The participants in these methods are considered the rational contenders perfectly. The planned result is the purely rational optimal solution and does not express the noncomplete rational decision-making preference for drivers in the actual routing decisions. Although the questionnaire may be a handy pathway for cognizing driving preferences, it lacks efficiency and comprehensiveness.
The premise of learning driving preferences is to obtain an understanding about the roads conditions. The more we aware of road properties, the more satisfied we cognise the personalized preferences. MapLSTM can have a fine-grained cognition of road traffic conditions, so we can learn the driving preferences easily. For drivers of vehicles, there are two preferences getting the most attention: time and distance. Figure
Segment-based traffic information at a certain time.
Routes with different driving preferences from A to B.
Navigating vehicles to their destination is an important service for ITS. In addition to using historical and real-time traffic conditions, the state-of-the-art systems take into account the impact on the future traffic conditions which can be obtained by predicting. For example, the method in [
As mentioned above, the existing methods are still laborious for lightweight, fine-grained, and accurate prediction. So we propose MapLSTM to predict traffic conditions effectively. We analyze and compare the use about the predicted traffic conditions in navigation planning, as in Table
Applications and comparisons about the predicted traffic conditions in navigation planning.
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[ | smart sensors | city | self-aware | Gaussian Process Regression | middle | middle | middle | low |
[ | GPS points | region | autonomous | Value Iteration Network | middle | middle | high | middle |
[ | street-view images | intersection | autonomous | CNN+RL+ | high | middle | high | low |
[ | GPS points | region | agents | Ant Colony+RL | middle | middle | middle | middle |
[ | vehicles sharing | city | RIS | statistics | low | low | middle | low |
In the COPERT model [
As in Figure
Interring traffic emissions of 126th road segment.
Table
Applications and comparisons about the traffic prediction.
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| [ | web camera | traffic density | short | Convolutional neural network | high | fine-grained | intersection |
[ | an open dataset | traffic flow | long/short | Generative adversarial network | high | coarse-grained | freeway | |
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| [ | web camera | traffic density | short | Fully convolutional networks | high | fine-grained | restricted area |
[ | an experimental car | vehicle speed | short | Auto-regressive model | middle | fine-grained | road segment | |
[ | floating car | vehicle speed | short | HMMs+SUMO | middle | coarse-grained | motorway | |
[ | Loop Detector | traffic volume | short | ST semi-supervised learning | low | fine-grained | road segment | |
[ | traffic loops | traffic flow | long | Gaussian process regression | low | coarse-grained | region |
Urban road traffic system is the lifeblood of a city, which ensures its operation. Predicting traffic conditions for road segments is the prelude of working on intelligent transportation. In this paper, we proposed MapLSTM, a traffic predicting mechanism for road segments, to promote the development of ITS. MapLSTM can accelerate the landing of many applications in a lightweight and fine-grained way. In the future, autonomous humanlike driving based on road topography is worth concern, and we will focus on complex spatial correlations in traffic environment.
We used the source code of ConvLSTM in our paper; the URL is: “https://github.com/carlthome/tensorflow-convlstm-cell.” Moreover, we used the dataset “T-Drive Taxi Trajectories” released by MSRA; the URL is “https://www.microsoft.com/en-us/research/project/urban-computing.” There is just one week of data in released dataset. Although one week of data can also conduct secondary analyses, we used one month of data of “T-Drive Taxi Trajectories” in our experiments for better performance, in which data was from the previous cooperation project.
The authors declare that they have no conflicts of interest.
This work is supported by the Natural Science Foundation of Beijing under Grant no. 4181002, and the Natural Science Foundation of China under Grant no. 61876023.