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This study is aimed at forecasting traffic on King Fahd Causeway which carries cross border traffic between Kingdoms of Saudi Arabia andBahrain.Two concerns were identified inthe above mentioned study, i.e. use ofstock market indices for predicting daily traffic and use of Artificial Neural Networks (ANNs).The focus of this research was to investigate the effects of readily available non-traffic parameters on border transport prediction in this region.Hence this study makes use of readily available non-traffic parameters for predicting border traffic.Following that, other non-traffic parameters, including weather, vacation and salary periods, and other time-related parameters, were used, withthepredictedweeklyADT,inthemodelforpredictingdailytraffic.Keeping theabove factors inmind, thestockmarketprices of Saudi Arabia and Bahrain were used to predict weekly Average Daily Traffic (ADT) for border transport between these two countries.Moreover, time series traffic data was used in developing the prediction models by El-alfy et al. (2015) in their study, which is not feasible, as mentioned above.This study also provides the comparison of urban and border traffic prediction parameters and approaches.Furthermore, time-series data for predicting cross-border transport may not be viable since non-traffic parameters may cause unprecedented change in traffic pattern.Moreover, the continuous collection of traffic data for long periods requires substantial resources and state-of-the-art technology for accurate traffic counts (Gramaglia et al., 2014).Therefore, a prediction model based upon non-traffic parameters may be more convenient in this case.


Original text

This study is aimed at forecasting traffic on King Fahd Causeway which carries cross border traffic between Kingdoms of Saudi Arabia andBahrain. Keeping theabove factors inmind, thestockmarketprices of Saudi Arabia and Bahrain were used to predict weekly Average Daily Traffic (ADT) for border transport between these two countries. The weeklyADTwaspredictedusingstockmarketindicesbecausetheeffect of these indices (political and economic situation) will usually take a few days and will not show on micro scale such as daily traffic. Following that, other non-traffic parameters, including weather, vacation and salary periods, and other time-related parameters, were used, withthepredictedweeklyADT,inthemodelforpredictingdailytraffic. The focus of this research was to investigate the effects of readily available non-traffic parameters on border transport prediction in this region. These parameters may cause changes in travel demand, which will not be shown in the periodic trend of traffic. Moreover, the continuous collection of traffic data for long periods requires substantial resources and state-of-the-art technology for accurate traffic counts (Gramaglia et al., 2014). The data collection technologies are undergoing changes with time, but there is still a lack of integration between travel demand modelers and practice communities (Lee et al., 2016; Kisgyörgy and Vasvári, 2017). Furthermore, time-series data for predicting cross-border transport may not be viable since non-traffic parameters may cause unprecedented change in traffic pattern. Therefore, a prediction model based upon non-traffic parameters may be more convenient in this case. This study is inspired by a previous study, done in the same area, by El-Alfy et al. (2015). It is found to be a pioneering study on the use of stock market prices for border traffic forecasting. Two concerns were identified inthe above mentioned study, i.e. use ofstock market indices for predicting daily traffic and use of Artificial Neural Networks (ANNs). The effects ofnationaleconomic andpolitical situations, which is surrogated by stock prices, may not always be affecting travel demand on a daily basis. It seems more logical to expect that historic data of prices for a longer time span (week or fortnight) are beneficial in predicting traffic. Secondly, ANNs do not give insight about the relationship between the predicted value and its predictors. Regression models would be a better approach considering their explanatory powers established through statistical bases. These concerns have been addressed in this research. Moreover, time series traffic data was used in developing the prediction models by El-alfy et al. (2015) in their study, which is not feasible, as mentioned above. Hence this study makes use of readily available non-traffic parameters for predicting border traffic. This study also provides the comparison of urban and border traffic prediction parameters and approaches.

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