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dc.contributor.authorSaidi Siuhi
dc.contributor.authorMohamed Kaseko
dc.descriptionSiuhi, S., & Kaseko, M. (2016). Incorporating vehicle mix in stimulus-response car-following models. Journal of Traffic and Transportation Engineering (English Edition), 3(3), 226-235.
dc.description.abstractThe objective of this paper is to incorporate vehicle mix in stimulus-response car-following models. Separate models were estimated for acceleration and deceleration responses to account for vehicle mix via both movement state and vehicle type. For each model, three sub-models were developed for different pairs of following vehicles including “automobile following automobile,” “automobile following truck,” and “truck following automobile.” The estimated model parameters were then validated against other data from a similar region and roadway. The results indicated that drivers' behaviors were significantly different among the different pairs of following vehicles. Also the magnitude of the estimated parameters depends on the type of vehicle being driven and/or followed. These results demonstrated the need to use separate models depending on movement state and vehicle type. The differences in parameter estimates confirmed in this paper highlight traffic safety and operational issues of mixed traffic operation on a single lane. The findings of this paper can assist transportation professionals to improve traffic simulation models used to evaluate the impact of different strategies on ameliorate safety and performance of highways. In addition, driver response time lag estimates can be used in roadway design to calculate important design parameters such as stopping sight distance on horizontal and vertical curves for both automobiles and trucks.en_US
dc.subjectCar Industryen_US
dc.titleIncorporating vehicle mix in stimulus-response car-following modelsen_US

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