Kenneth M. Vaughn's research while affiliated with University of California, Davis and other places

... Numerous studies during that period investigated the behavioral responses of travelers to the provision of travel information, see Schofer et al. (1993) for a global review. Choice experiments in the laboratory using interactive computer simulation in particular gave more information on traveler motivation and compliance with ATIS (Yang et al., 1993;Adler and McNally, 1994;Vaughn et al., 1995;Mahmassani and Liu, 1999;Adler, 2001). Now that en-route navigation systems are a common feature of individual cars, this question has received renewed attention in the literature. ...

... Often the collected data are concerning the attributes of the traveller and the trip since the actual route is rather difficult to obtain in this way. Mahmassani et al. (1993) and Abdel-Aty et al. (1995) described different approaches to the data collection by means of questionnaires. ...

... Reference [1] estimated two route choice models; first, five hypothetical binary choice sets were used collected by a computer-aided telephone interview to determine how travel time variation affects route choice and second, three binary route choice SP scenarios were used collected by a mail survey to investigate the potential effect of advanced traveler information systems on route choice. Commuters' diversion and return behavior varied with their personal characteristics [14][22] and user knowledge of the network and the available paths [19]. It was established from the literature that, socio-economic characteristics [10][16][18][20], numbers of stops [11][15][19], traffic incident and congestion information [9][17][19][20] are the most important in influencing subjects' in route decisions. ...

... Dell'Orco and Teodorovic, 2009;Emmerink et al., 1996;Iida et al., 1992;Khattak et al., 1995;Mannering et al., 1994;Tsirimpa et al., 2007;Yang et al., 1993), safety implications (e.g. Srinivasan et al., 1995;Al-Deek et al., 1998), and the efficiency of road usage (e.g. Al-Deek and Kanafani, 1993;Emmerink et al., 1995a,b;Mahmassani and Jayakrishnan, 1991), and so on. ...

... Information plays a very important role in the travelling decisions of individuals [1] [2]. This role have been investigated in several contexts [3] including route guidance [4], provision of tourist information [5] and highway congestion and incident related information [6]. In itinerary planning for performing daily activities, determining how and when to reach activity locations in an efficient way considering constraints of decision making is an important issue people are faced. ...

... Wei at el. (Wei et al., 2014), applied reinforcement learning and multi-agent simulation to incorporate travelers' memory, learning rate and experience cognition to the route choice behavior model. Yang et al. (Yang et al., 1993) adopted neural network model on 32 simulated days where human subjects chose between a freeway or side road, and their model showed consistent 75% of the accuracy for route selection prediction. While discrete choice models have better interpretability, the interpretation results are largely depended on the quality of experimental designs and alternative route simulation algorithms and limited to the restricted model complexity and incapacity to capture the rich variations in route choice behaviors. ...

... Neural network (NN) has been used by many researchers to study the problems in transportation domain [10][11][12]. It is one of machine learning methods and simulates the process that how the human brain works. ...

... Advanced Traveler Information Systems (ATIS) that provide travelers with up-todate traffic information, are generally believed to be efficient in many aspects such as improving individual trip planning, alleviating road congestion and enhancing traffic network performance. There have been substantial developments for modeling and assessing qualitatively and quantitatively the effects of ATIS on travelers and the transportation system, see the review papers or reports by Boyce[13], Gardes and May[16], Abdel-Aty et al.[1], Bonsall[12], Emmerink et al.[18]Author for correspondence. and Watling[38]. ...

... An interesting finding of the study was that males and high-income commuters were unlikely to be influenced by traffic information. Abdel-aty et al. [1] found out an important relationship between the use of traffic information and the propensity to change routes through a statistical analysis of commuters' route choice behavior. Important relationships relating the influence of commuters' socio-economic characteristics and the level of congestion on traffic information use and route-change frequency are also examined. ...

... Literature shows that both the traffic situation and characteristics of the traveller influence the required types and quality level of travel information (De Palma et al., 2012). Vaughn et al. (1993) show that less experienced drivers (in terms of travelling frequency) tend to comply more with travel information than more experienced drivers. As experience increases, travellers are more reluctant to make use of travel information and tend to prefer routes with lower average travel times but greater travel time variance . ...