SHORT-TERM TRAFFIC VOLUME FORECASTS:
Existing And Future Research

Pawan Lingras
Mathematics and Computing Science
Saint Mary's University, Halifax
Nova Scotia, Canada, B3H 3C1.
	S. C. Sharma
Faculty of Engineering
University of Regina, Regina
Saskatchewan, Canada, S4S 0A2.

ABSTRACT

New technologies under the title of Intelligent Transportation Systems (ITS) are expected to alleviate the ever increasing problem of congestion in modern transportation systems. Intelligent Vehicle Highway System (IVHS) is one such emerging technology designed to improve the quality of travel in a highway system. Researchers are proposing various approaches to an IVHS. All of these approaches require the prediction of travel conditions in the short-term future. One application that is being implemented in various places, called Advanced Traveler Information Systems (ATIS), relies significantly on the projected travel conditions. This paper reports a survey of ATIS related research including various approaches used to predict traffic volumes in the short-term future. The discussion includes objectives of the modeling process, modeling techniques, choices of input and output parameters.
1.0	
INTRODUCTION

Intelligent Transportation Systems (ITS) are considered to be a solution for problems such as increasing highway congestion, continuing threats to travel safety and security, and increasingly constrained transportation agency budgets (U.S. Department of transportation, 1999). Advancements in computer and communications technologies are making it possible to address several challenges in transportation, such as safety, productivity, and mobility. One of the ways of alleviating the congestion problems is to redistribute the traffic over the highway network. This redistribution can be spatial as well as temporal. If travelers have advanced information about the traffic conditions, they can reschedule or reroute their trips. One of the important issues in an Advance Traveler Information System (ATIS) is accurate forecasting of traffic conditions in the short-term future. 

In past studies, different methodologies and techniques have been used to forecast traffic volumes (Davies, et al., 1990; Florio and Mussone, 1995; Kaysi, et al., 1993; Lingras and Osborne, 1997; Ulbricht, 1993). A number of forecasting methods are related to time series analysis in which prediction of the future value of a variable is based on past values of the same variable. The time series models identify the pattern in the past data and extrapolate that pattern into the future. Some researchers have used various methods, such as Box-Jenkins auto-regressive integrated moving average model, to predict traffic volume one minute ahead of time on major urban arterials. Others have developed and tested such forecasting models as historical average, neural network and non-parametric regression models for the purpose of forecasting traffic flow 15 minutes into the future. Another researcher developed multi-recurrent neural network models to forecast the number of cars passing on a highway checkpoint between 7 a.m. and 8 a.m. and compared the results with various statistical methods.

The proposed paper provides a detailed overview of some of the research on prediction of traffic volumes in an Advance Traveler Information System. This past experience of other researchers along with authors' own experience is used to discuss the directions for further research. The discussion includes:
Section 2.	Characteristics of an ATIS.
Section 3.	Traffic forecasting requirements of an ATIS.
Section 4.	Merits of various statistical and neural network techniques.
Section 5.	Choice of input and output parameters.
Section 6.	Summary.

2.0	CHARACTERISTICS OF AN ATIS

Intelligent vehicle highway Systems (IVHS) is one of the important topics of research in the field of Intelligent Transportation Systems (ITS). Dynamic traffic management schemes that make use of available and anticipated advanced technologies are expected to achieve efficient operations of existing road networks. Researchers envision linking of road infrastructure, vehicles, and drivers using advanced communication technology, computers, information display equipment, and traffic control systems (Kaysi, et al., 1993). Advanced Traveler Information Systems (ATIS) are expected to play a major role in reducing traffic congestion and improving traffic flows and safety. An ATIS can provide information to motorists about alternative paths using roadside signals and on-board information systems, which will help reduce delays caused by both incident and recurrent congestion. The aim of an ATIS is to optimize driver route selection in response to real-time road and traffic conditions.

Kaysi,  et al. (1993) provide comprehensive description of a framework for the operation of an ATIS. The framework proposed by Kaysi, et al. has three main modules:

A.	Surveillance System: This will consist of traffic sensors, video cameras, and other facilities. The collected data may consist of flows, speed, travel times, numbers of queued and moving links on each highway section, and detected incidents.
B.	Congestion Prediction (COP) Module: This module will be responsible for projection of traffic conditions.
C.	Control and Routing (CAR) Module: This module generates the advice based on information supplied by the surveillance systems and COP module.

The data used by the ATIS will consist of:
a.	Infrastructure data: This data changes very slowly with time and consists of information such as network topology and capacity of each network element or highway section in the network.
b.	Historical origin-destination (O-D) data: This data is updated periodically using surveys and inferences from traffic counts.
c.	Updated O-D data: The latest information obtained from surveillance in combination with historical O-D data can be used to generate the three dimensional O-D matrices for subsequent time periods.
d.	Surveillance data: This includes information obtained from the surveillance system such as traffic volume, speed, vehicle occupancy, and the presence of incidents.

Some of the details about the framework suggested by Kaysi, et al. appear in the subsequent sections. The rest of this section describes two projects that are actively implementing some features of an ATIS in the design of intelligent vehicle highway systems. The information is obtained from the official web sites related to these projects and is current as of January, 1999.

2.1	Washington State Transportation Center

Washington State Transportation Center (1994) identified a need to assess how travelers accepted and used the travel information currently provided through various public and private media, such as broadcast media, highway advisory radio, telephone hotlines. 

This project, which began in February 1993, provides an evaluation of four Advanced Traveler Information Systems (ATIS) in Washington State. These systems are:

•	Flow,
•	Traffic Reporter, 
•	Bellevue Smart Traveler, and 
•	the proposed Canadian Border Crossing Information System.

To accomplish this evaluation, an ATIS taxonomy was developed, and guidelines were  established for evaluating ATIS in each of the taxonomy's categories. The classification of systems, including their distinct definitions of success, is complete.

Three related but distinct system goals have been identified as contributing to the overall ATIS goal of increased mobility: 

•	Removing individual drivers from congested roads 
•	Reducing individual driver's overall use of roadways
•	Altering drivers' modes of transportation
 
The four ATIS were reviewed and classified in accordance with these goals as a basic framework, with an analysis of the functional elements of message type, technology used, and location of the system. The use of the taxonomy was assessed in terms of its ability to guide effective evaluation of other ATIS projects. 

2.2	Minnesota Department of Transportation

For past several years, the Minnesota department of transportation has also been actively pursuing the Intelligent Vehicle Highway Systems project. Since its inception in 1991, Minnesota Guidestar has performed needs assessments, research and development, and full scale operational testing of ITS strategies and technologies. Such efforts have led to the commitment of more than $13.5 million in private sector contributions and $49.9 million in Federal funding that complement state and local resources for ITS projects in Minnesota. 

A large part of the Minnesota Guidestar program's early work focused on the Twin Cities metropolitan area. Numerous operational tests evaluated a variety of ITS concepts and technologies. The Twin Cities is now the focus of the Orion model deployment initiative. Orion activities cut across a wide variety of functional areas and emphasize system integration to create an Intelligent Transportation Infrastructure (ITI). 

In an effort to move toward statewide ITS deployment, Minnesota Guidestar completed a Rural ITS Scoping Study in 1994. The study identified and prioritized the needs of travelers in Greater Minnesota. 

In 1995, the Polaris Statewide Architecture Project outreach activities confirmed and expanded upon the Rural Scoping Study findings. The Polaris findings described the need for both rural and urban ITS applications in Minnesota and served as the basis for the development of a statewide ITS architecture. 

Also in 1995, Minnesota Guidestar developed a statewide business plan for Commercial Vehicle Operations (CVO) in Minnesota. Following a comprehensive process re-engineering efforts, an action plan was agreed upon with initiatives in: enforcement, business processes, information dissemination and computer systems. Minnesota was recently selected as one of seven states to receive Federal funding for CVO model deployment, named Commercial Vehicle Information Systems and Networks (CVISN). 

Over the next 10 years, Minnesota's transportation system will evolve to incorporate computerization and sensor technologies in vehicles and roadways. Vehicles will have internal navigation systems that will require enhanced traffic data collection systems and an up-to-date mapping infrastructure. In-vehicle signing systems will begin to replace the current, fixed traffic advisory signs. Vehicles will begin to incorporate latitudinal and longitudinal control devices that will need infrastructure guidelines or markers. 

Real-time, route-specific traveler information systems will replace the current area-wide, fixed-time reports. Travelers will be able to use personal communication devices, televisions, telephones and in-vehicle devices to receive the information they desire. Information will be available on route-specific travel times, parking availability, emergency services, mode schedules and yellow pages type information. 

During the next two years, the Twin Cities metro area will begin to investigate the integration of traffic operations centers from Mn/DOT, Minneapolis, St. Paul, the State Patrol, the Metropolitan Transit Commission, emergency services and others. This integration will produce a unified transportation operations concept while maintaining essential jurisdictional autonomy. The process to achieve this end will include appropriate pre-engineering studies and selection of contributing private sector partners. 

Safety gains will be looked at for demonstration in both the rural area and metro area. The State Patrol along with the Mn/DOT District Offices will play a lead role in these initiatives. 

3.0	TRAFFIC FORECASTING REQUIREMENTS OF AN ATIS

Previous section indicates the active pursuit towards reliable ATIS systems by various highway agencies. Projecting traffic conditions in the near-term future is arguably one of the important issues in an ATIS. Some of the researchers prefer to use only the historical data for vehicle guidance. In particular, Papageorgiou and Messmer (1991) claim that their route guidance based on current traffic conditions provides acceptable results. However, they caution that their approach may not work in highway networks which do not show stable traffic patterns. Many researchers stress the importance of including projected traffic conditions (Catling and McQueen, 1991; French, 1990; Kaysi, et al., 1993). A route guidance module such as the one outlined by Kaysi, et al. (1993) will make recommendations to the drivers based on the fastest path through the highway network.

The time required to travel a particular section of highway depends on the traffic conditions when the vehicle gets on that particular highway section. Let us picture a vehicle traveling on a path shown in Figure 1. Path A, B, C, D, E will be taken by vehicles while travelling from A to E. The table following the graph shows the travel times during different times of the day. The travel time for a vehicle starting from A to E will depend on the time when the vehicle leaves node A. For example, if a vehicle leaves A at 8:00 a.m. then it will reach B at 8:15 a.m., C at 8:30 a.m., D at 8:45 a.m., and  arrive at the destination E at 9:00 a.m. The total travel time for the vehicle from A to E will be 60 minutes, if the travel commences at 8:00 a.m. On the other hand, if the vehicle leaves A at 8:30 a.m. then it will reach B at 8:40 a.m., C at 8:50 a.m., D at 9:00 a.m., and  arrive at the destination E at 9:10 a.m., making up a total travel time of 40 minutes. Even though Figure 1 uses a hypothetical simplistic example, many urban commuters are familiar with scenarios in which leaving 30 minutes late from home result in only 10 minutes delay at work.

The actual problem is further complicated by the fact that the future travel times such as the ones shown in Figure 1 are in fact not known ahead of time. An ATIS will have to use predictions for these future travel times. These predictions are bound to change as a vehicle begins its travel. For example, predictions made at 8:00 a.m. for the travel time on a highway section for the period between 9:00 - 9:15 a.m. may be different from the predictions for the same period made at 8:30 a.m. If the modeling process is reliable, the latter predictions (made at 8:30 a.m.) will be more accurate than the earlier predictions (made at 8:00 a.m.). If projections of travel times change significantly, it may be necessary to reassess the route that was chosen at the beginning of the journey. However, slight change in the travel time projections should not warrant the change of route. That means the route guidance systems should use minimum and maximum values of projected travel times in determining the best route for a particular trip. If the future predictions are in the same range, it will not be necessary to change the route.

4.0	CHOICE OF INPUT AND OUTPUT PARAMETERS

The previous section provides some guidance about the output variables of a traffic prediction model in an ATIS. Since the route guidance needs to take into account the travel time for a vehicle on a highway section when the vehicle is expected to arrive at the highway section, it is necessary to project traffic conditions for several time periods in the near future. In order to cover all the requirements of the vehicle guidance module, it will be necessary to have travel time predictions starting at x minutes in to the future and going up to y minutes in to the future. Here x is the smallest travel time of any highway section and y is the largest travel time of any route in the network. For example, in Figure 1, the shortest travel time for any highway section is 10 minutes, while the longest travel time is 60 minutes for the route A-B-C-D-E. However, if one were to allow users to choose the time of departure ahead of time, it will be necessary to predict the travel time further in to the future. For example, if a user were to decide the time of departure up to 3 hours ahead of time, it will be necessary to add three hours to the value of y. In fact, it may be convenient to maintain a table of projected travel times for the next 24 hours. In order to simplify the modeling process, it may be possible to ignore the late night and early morning hours. The travel time projections will have to be modified at a regular interval of 15 minutes to one hour. If the route determination were to be based on the projected range of travel time values, the models will have to output the range of values instead of a single precise value.

In order to calculate the travel times for a given highway section, the prediction models will project the traffic volume on that highway section for a given time period. The travel time will then be calculated as a function of traffic volume and capacity of the highway section. Note that the capacity of the highway section will have to take into account the known incident information.

Input parameters for a given highway section depend upon the type of highway. Typical input variables will be as follows:

i.	Previous 24 hours (one day) or 168 hours (one week) of traffic patterns
•	It is possible to reduce the number of these variables by eliminating late night and early morning traffic volumes.
•	Further reduction is also possible. However, traffic volumes from previous day and from the same day of the previous week are essential in any model.
ii.	Some highway sections have significant special event traffic such as long-weekend recreational traffic, or football game traffic. In that case, traffic patterns from similar events in the past (usually previous year) should also be used as input parameters.
iii.	If it is possible to clearly identify the highway sections that feed traffic to a given highway section, then the traffic volumes from immediate past for these feeder highways will also be used as influencing input parameters. Figure 2 shows an example of such a highway section. Highway sections A-D, B-D and C-D all feed traffic to the highway section D-E. Therefore, traffic volumes from previous time periods on A-D, B-D and C-D will be used as input parameters in determining the projected traffic volumes on D-E.
iv.	Finally, the latest data on incidents on the highway section being modeled should also be used as input to the model.

5.0	MERITS OF VARIOUS MODELING TECHNIQUES

Researchers are experimenting with a wide variety of techniques for the prediction of traffic volumes and travel times on a given highway section. The following is a partial list of techniques that are being studied by various researchers.

I.	Neural Networks
•	Conventional Neural Networks
•	Multi-Recurrent Neural Networks 
•	Time Delay Neural Networks
•	Rough Neural Networks
II.	Statistical Techniques
•	Regression Analysis
•	Autoregression analysis
•	Moving average method
III.	Traffic assignment models
•	Dynamic Traffic Assignments

The results from application of these techniques are mixed. Due to space limitations, it is not possible to provide a comprehensive comparison of these techniques. There are proponents for all three types of models. It is also possible to combine two different models to improve the accuracy of predictions. For a particular implementation of an ATIS, it will be necessary to try out different models until an acceptable level of accuracy is achieved. It is important to make sure that the model chosen provides many of the characteristics required for the short-term traffic flow forecasting. Some of the characteristics of a model for the prediction of traffic volumes are as follows: 

•	Adaptive: The traffic patterns constantly change. As the ATIS gain popularity, the traffic volume predictions will induce drivers to change their route selections, which will affect the accuracy of predictions. It is necessary that the model adapt itself to the changing nature of traffic flows. 
•	Ability to process a time series: Time series analysis uses the same variable for input as well as output. For example, traffic volume will be a input as well as output variable in our model. The difference is that the output is the future value of the variable, while inputs are the past values of the variable. In this case, the input variables are not independent. Hence, some of the statistical methods may not be applicable. The techniques specifically developed for time series analysis, such as autoregression, moving average, time delay neural networks, can be exploited to their fullest potential.
•	Ability to output a range of values as opposed to a single precise output: As mentioned before, the route guidance should be based on projected upper and lower bounds of travel times. As long as the future predictions stay within that range, the selected route will not be changed. Rough neural network is one of the techniques that provides such an output. There are other popular statistical techniques, which also predict upper and lower bounds.
•	Resistance to noise: For a variety of reasons such as equipment malfunction or bad weather, there are occasional abnormal changes in traffic patterns. These abnormal changes are called noise in the data. The prediction model should be able to make reasonable predictions even in the presence of such noise. Lingras and Osborne (1997) found that autoregression and neural network models showed robust behavior in the presence of small amount of noise. However, if the amount of noise increases, neural networks are expected to be more robust than statistical models.
•	Fast computation speed: Since the models are going to be used to make decisions in real-time, the speed of computation is important. Even though the training time of a neural network can be computationally expensive, the training will be typically done off-line. The trained neural network will be used for prediction on-line in real-time. The trained neural networks work at least as fast as any other statistical model. 

6.0	SUMMARY

Advance Traveler Information Systems (ATIS) will play an important role in the Intelligent Vehicle Highway Systems (IVHS). This paper reports a survey of proposed framework, and implementations of ATIS. Projecting travel conditions in short-term future is one of the important issues in an ATIS. The paper describes the input and output requirements of such models. Some of the important characteristics of the models along with a partial list of possible techniques is also included. 

REFERENCES

Catling, J. and McQueen, B. Road Transport Informatics in Europe - Major Programs and Demonstrations. IEEE Transactions on Vehicular Technology, Vol. 40, No. 1, pp. 132-140, 1991.
Davis, A.G., Nihan, N. L., Hamed, M. M., and Jacobson, L. N. Adaptive Forecasting of Freeway Traffic Congestion. Transportation Research Records, 1287, Transportation Research Board, Washington D.C., pp. 29-33, 1990.
Florio, L., and Mussone, L. Neural Network Models for Classification and Forecasting of Freeway Traffic Flow Stability. Transportation System: Theory and application of advanced Technology, Vol. 2, pp. 773-784, 1995.
Fox, J. A. The Effects of Information Accuracy on User Trust and Compliance. http://www.acm.org/sigchi/chi96/proceedings/doctoral/Fox/jef_txt.htm, 1996.
French, R. Intelligent Vehicle/Highway Systems in Action, ITE Journal, pp. 23-31, Nov. 1990.
Lingras, P.J and Osborne P. Effect of noise on regression and neural network predictions, Proceedings of the Conference of Canadian Society of Civil Engineers, Sherbrooke, Quebec, June, pp. 331-339, 1997.
Minnesota Department of Transportation. Minnesota Guidestar : Shaping Tomorrow's Transportation System Today. http://www.dot.state.mn.us/trim/ats/guidestar/, 1998.
Kaysi, I., Ben-Akiva, M., and Koutsopoulos, H. An Integrated Approach to Vehicle Routing and Congestion Predictions for Real-time Driver Guidance. Transportation Research Records, 1408, Transportation Research Board, Washington D.C., pp. 66-74, 1993.
Papageorgiou, M. and Messmer, A. Dynamic Network Traffic Assignment and Route Guidance Via Feedback Regulations, Transportation Research Records, 1306, Transportation Research Board, Washington D.C., 1991
Washington State Department of Transportation.  Puget Sound Traffic Conditions: North-up System View. http://www.wsdot.wa.gov/regions/northwest/NWFLOW/, 1999.
Smith, B. L., and Demetsky, M. J. Traffic Flow Forecasting: Comparison of Modeling Approaches. Journal of Transportation Engineering, American Society of Civil Engineers, Vol. 123, No. 4, pp. 261-266, 1997.
ITS Research Program: University of Washington. TrafNet: Real-Time Seattle Area Traffic Conditions over the Internet. http://www.its.washington.edu/trafnet/ , 1997
U.S. Department of transportation. Intelligent Transportation Systems: An Overview.  http://www.its.dot.gov/ , 1999.
Ulbricht, C. Multi-recurrent Networks for Traffic Forecasting. Technical Report, Austrian Research Institute for Artificial Intelligence, Vienna, Austria, 1993.
Washington State Transportation Center. Advanced Traveler Information Systems. http://www.ota.fhwa.dot.gov/tech/ma/ap71.html, 1996.

LIST OF FIGURES

FIGURE 1. TRAVEL TIME CALCULATIONS 

A

B

C

D

E

Time	A-B	B-C	C-D	D-E	
8:00 - 8: 15 a.m.	15	10	10	10	
8:15 - 8:30 a.m.	10	15	15	15	
8:30 - 8:45 a.m.	10	10	15	20	
8:45 - 9:00 a.m.	10	10	10	15	
9:00 - 9:15 a.m.	20	10	10	10	
9:15 - 9:30 a.m.	15	10	10	10	

FIGURE 2. INFLUENCE OF FEEDER HIGHWAY SECTIONS

A

B

C

D

E