Traffic volume is a fundamental variable in several transportation engineering applications. For instance, in transportation planning, the annual average daily traffic (AADT) is a primary element that has to be estimated for the year of horizon of the analysis. The huge amounts of money to be invested in designed transportation systems are strongly associated with the traffic volumes expected in the system, which means that it is important that the AADT should be accurately predicted. In this paper, a modified version of a pattern recognition technique known as support vector machine for regression (SVR) to forecast AADT is presented. The proposed methodology computes the SVR prediction parameters based on the distribution of the training data. Therefore, the proposed method is called SVR with data-dependent parameters (SVR-DP). Using 20 years of AADT for both rural and urban roads in 25 counties in the state of Tennessee, the performance of the SVR-DP was compared with those of Holt exponential smoothing (Holt-ES) and of ordinary least-square linear regression (OLS-regression). SVR-DP performed better than both methods; although the Holt-ES also presented good results.
Traffic volume is the basic element in transportation engineering. Basically, all transportation engineering projects involve traffic volume as a key input, including signal timing, geometric design, pavement design, transportation planning, highway improvement, congestion management, roadway maintenance, air pollution modeling, emergency evacuation plans, among others. Many transportation resources, such as the ASSHTO guidelines for traffic data programs (AASHTO, 1992), outline a large number of transportation engineering activities that require estimates of traffic volume demand parameters such as the annual average daily traffic (AADT).
The concept of AADT is simple: Roess, Prassas, and McShane (2004) define AADT as “the average 24-h volume at a given location over a full 365-year (366 in a leap year)”. In other words, AADT is the average number of vehicles that pass a roadway section each day in a particular year.
State departments of transportation (DOT’s) and local transportation agencies commonly have collected and predicted AADT for a variety of design, planning, and administrative purposes (Seaver, Chatterjee, & Seaver, 2000). These governmental agencies commit a large amount of time and funding to maintain their traffic volume data collection programs (Sharma, Lingras, Xu, & Liu, 1999), commonly known as traffic counting programs. In these programs, the AADT data for various locations can be measured by using permanent traffic counters. However, for most cases, comprehensive 365-day data collection is not economically feasible, such as in local roads in rural areas, nor even possible in cases where AADT for future-years is needed. In these cases AADT has to be predicted.
A modified support vector regression (SVR) approach has been proposed for future-year AADT estimation. The modified SVR uses data-dependent parameters in order to reduce computational time and to achieve better predictors. This can also widen the application of SVR in intelligent transportation systems especially for dynamic decision support systems. The comparison results showed that SVR-DP outperformed the OLS-regression technique, which is commonly used for future-year AADT forecasting purposes. The SVR-DP also performed better than the Holt’s ES, but one can argue that both techniques performed similarly. The outstanding performance of SVR-DP can be attributed to the remarkable characteristics of SVR and the incorporation of a data-dependent procedure for computing SVR parameters. This reduces uncertainty relating to parameter selection and computation time.
Holt’s ES also presented a good forecasting capability. For some transportation agencies, such as DOT and MPO, Holt-ES might be the preferred option due to its simplicity in terms of both implementation and interpretation. However, transportation analysts should be aware that Holt-ES has two significant limitations: it does not perform well for long forecast horizons nor for data with seasonality. Therefore, Holt-ES should be avoided for longer AADT forecast horizon terms. These two constraints are not part of the SVR-DP technique.
Furthermore, the theoretical foundation SVR-DP is ideal for traffic volume forecasting. By computing the SVR parameters based on the distribution of past data, the SVR model can exploit information contained in a large set of data. As a result of large variations in traffic volume, the modified SVR approach is likely to incorporate unexpected changes in the model and attempt to create one single mapping function. Future work is ongoing to extend the theoretical foundation of SVR to short-time traffic volume predictions by accounting for some of the uncertainties associated with such models.
In conclusion, the SVR-DP technique provides an accurate forecasting technique where no external explanatory variable is used. This can be seen as very advantageous because the inclusion of external variables might not be feasible. Besides, for future-year AADT estimation studies such as the one presented here, external variables would have to be predicted into the future and this might add a significant error to the model.
6.1. Recommendations for future research
The forecasting performance of the SVR-DP should be further investigated. The authors recommend that future research be directed in the following ways. The proposed methodology could be applied for longer forecast horizons. For many transportation planning applications, AADT needs to be predicted many years into the future. The methodology also could be applied for short-term traffic prediction for ITS operations. For that purpose, the online version of SVR has been implemented by the authors in another study. Finally, for large geodatabases such as the ones generated by traffic counting programs, a new methodology combining spatial data analysis and SVR could be investigated.
