Journal of Advanced TransportationVolume 49, Issue 2 p. 171-173 EditorialFree Access Special issue: emerging technologies for intelligent transportation First published: 09 February 2015 https://doi.org/10.1002/atr.1304AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Recent rapid development of intelligent transportation systems (ITS) makes it possible to improve the efficiency and reliability of transportation networks. The combined application of various technologies and state-of-the-art methodologies for ITS development holds great promise for increasing the reliability of existing road networks through more efficient road use, which can also provide area-wide traffic data, travel time information, route guidance, traffic management, urban traffic control, and so on. Therefore, emerging technologies for ITS affect the community in a number of different ways including transportation network performance, transportation planning, and management practice as well as transportation industry. Susilawati et al. 1 commented that reliability is an important factor for analysis of route, mode, and departure time choices and is a key performance indicator for intelligent transportation systems. Wang and Khattak 2 indicated that there is spatial heterogeneity in information acquisition and user decisions. A better understanding of how consumers acquire dynamic traveler information to adjust their travel behavior is a key component of developing ITS emerging technologies. Bekhor et al. 3 showed that cellular phone technologies could be used to provide travel data for development of national travel demand model. Farooq et at. [4] assessed the impacts of ITS on various transportation industries in Michigan, USA, with the use of a well-established Leontief's input–output model. Recently, Ouyang 5 showed how advance demand information strategies would have the potential to counteract the bullwhip effect and reduce supply chain costs in practice. This special issue is devoted to the dissemination of research findings with focus on the technologies and their impacts for ITS development including advance models and solution algorithms. In total, seven papers are included in this special issue and are summarized as follows. Based on the deployment of automatic vehicle identification (AVI) technology in urban road network in Beijing, China, Feng et al. 6 proposed a particle filter method for vehicle trajectory reconstruction with the use of AVI and traffic count data. It was found that the spatial–temporal trajectory correction factors are critical to the vehicle trajectory estimation accuracy. In the proposed method, five correction factors are considered (i.e., correction factors for the path consistency, the AVI measurability criterion, travel time consistency, the gravity flow model, and the path–link flow matching model). The probabilities of the most likely trajectories are updated by the Bayesian method to approximate the “true” trajectory. The urban road network nearby the Beijing Olympic Park was selected for a case study, in which the Vissim simulator was used to create a complete set of vehicle trajectories for validation of the estimation results. It was reported that the accuracy of the resulting trajectory reconstruction is very dependent on the AVI coverage and captured information. Further study should be carried out with the use of the historical trajectory data so as to estimate the dynamic vehicle trajectory on real-time basis. In freeway network with variable speed limit (VSL) control, Yang et al. 7 proposed two proactive VSL control models for use on recurrently congested freeway segments. The embedded traffic flow relationships are adopted in the first proactive model to predict the evolution of congestion pattern and to optimize the speed limit on freeway segments with VSL. In order to capture driver responses to the VSL control, the second proactive model was proposed with the use of an embedded Kalman filter function. Both models have been investigated with different traffic conditions and two control objectives that are to minimize the travel time or the speed variance. The results of extensive Vissim simulation have revealed that both the proactive models can significantly reduce the travel time and the number of vehicle stops over the recurrent bottleneck locations at the selected freeway segments. However, it was found in the performance evaluation results of the two proactive models that the control objective of minimizing speed variance can offer the promising properties for field implementation. In order to make use of the proposed models in reality, there is a need to calibrate drivers response parameters based on observed field data. Using multi-source sensor data, Lu et al. 8 proposed a Kalman filter model for estimation of time-dependent origin–destination (OD) matrix that is a key input to the self-adaptive traffic control systems for real-time traffic management. In the proposed model, the dynamic relationship between traffic volume and OD structure deviation was utilized for iterative OD estimation, in which an improved traffic assignment approach was adapted to enable the traffic assignment matrix to be updated on real-time basis. A dual self-adaptive mechanism based on the Kalman filter method was used to calibrate the proposed model. A case study was carried out in the downtown road network of Kunshan City, China. The results show that the proposed model outperforms better than the traditional link-volume-based and turning-movement-based methods for dynamic OD estimation. The proposed model offers an effective way to utilize multi-source sensor data for time-dependent OD estimation and dynamic traffic assignment. Recent advances in various traffic detection technologies may open a new avenue of research for generation of the “big data.” The proposed model can be extended for using the “big data” to estimate the dynamic OD based on various data collected by multiple sources of advanced traffic sensors. With consideration of traffic demand variation, Zhang and Lou 9 formulated an integrated mixed-integer nonlinear programming model for coordinating semi-actuated signals on arterials. The vehicle actuation logic is modeled explicitly, including phase green extension, phase gap-out, max-out, and force-off. All decision variables (cycle length, offset points, phase sequences, green splits of the major phases and minimum green, unit green extension, and maximum green for the minor phases) are optimized in the proposed model simultaneously. Due to the large number of integer variables in the model formulation, two algorithms are employed to search for the optimal signal plans; one is the traditional genetic algorithm (GA), and the other is a GA-based heuristic so as to save the computation effort in the searching stages. A case study with a real but small arterial network is carried out to demonstrate the application of the proposed model and solution algorithms. Further works can be conducted to consider fully actuated signals in a large-scale arterial network with recurrent and non-recurrent traffic demand during peak periods. In view of the uncertainties in stochastic road traffic networks, Zhu and Ukkusuri 10 proposed a novel distance-based dynamic tolling model for stochastic road traffic networks. In the proposed model, the traffic demand input and the saturation capacity of toll links are generated randomly according to a given probability distribution to account for the uncertainties in traffic demand and supply. It was assumed that vehicles can communicate with infrastructure (V2I) under the connected vehicles environment; the control unit of tolling is modeled as an intelligent agent interacting with the stochastic time-dependent network environment using the stochastic cell transmission model so as to determine different distance-based tolling rates of vehicles once every 5 minutes. A binomial logit model is applied to model the choice of the toll and non-toll lane, but route choice has not been considered in the proposed model. The optimal tolling rate is obtained by using the R-Markov average reward technique algorithm. A case study based on the Sioux Falls network has been conducted with a given set of hypothetical toll links in which there are arterial and freeway links. It was found that congested arterial roads are likely to be candidate links for distance-based tolls as compared to the candidate freeway links in order to reduce the total travel time of the study network. The proposed model can be further extended to consider multiple objectives (e.g., total traffic throughput, delay time, traffic emissions, etc.) for optimization of the dynamic tolls with decision variables on locations of toll links and toll lanes as well. For assessing the impacts of new variable message sign (VMS) information on arterial road, Gan and Ye 11 proposed a random effect panel data logit model to identify factors that affect drivers' route choice decisions when the travel times of alternative expressway and competitive arterial road are given. The proposed model was calibrated on the basis of stated preference survey data collected among over 200 selected drivers in Shanghai, China. Correlations within repeated choices by the same respondent were investigated. The results show that the random effect model performs well in addressing the repeated observations, and there are evidences of common unobserved random factors affecting route choice behaviors of the same respondent. It was reported that driver decision would be significantly influenced by the new VMS information service at arterial road. It was found that factors such as driver experience, travel time saving of using arterial road, and occurrence of accidents on expressway are positively co-related with the usage of the arterial road, while factors such as the number of traffic signalized intersections on the arterial road are negatively co-related. In addition, different types of drivers such as taxi drivers and female drivers would behave differently in view of these factors. However, travel time reliability of alternative routes should also be considered for further studies. In order to capture the traffic speed variability in road networks, Wang et al. 12 proposed a generalized function to model the structured empirical traffic speed variance. The generalized traffic speed variance function captures the nonlinear and heterogeneous nature of a parabolic-shaped empirical variance for measure of the dispersion of space mean speeds among drivers. The proposed variance function is a modification of the speed-density curve but with additional parameters. The parameters of the proposed traffic speed variance function can be calibrated with the use of an iterative nonlinear least square algorithm (i.e., Levenberg–Marquardt). A series of logistic speed–density curve with varying from three to five parameters are examined and used in the proposed traffic speed variance function. With these parametric logistic speed–density relationships and their corresponding traffic speed variance functions, it is likely to develop a stochastic logistic speed–density model with consideration of both the mean and the variance of traffic speed so as to capture the traffic speed variability in road networks with uncertainties. Owing to the diversity of research and development (R & D) on emerging technologies for intelligent transportation systems, the papers presented in this special issue are by no means exhaustive. However, they do provide general coverage of various important areas of R & D on this subject with attention to the network efficiency and reliability. The editor hopes that this issue will bring state-of-the-art methodologies for development of ITS technologies to the attention of practicing engineers and researchers, and that it will inspire and stimulate new R & D opportunities and efforts in the field. After all, it is hoped that this would improve the planning, design, and operation of ITS and would help promote their use to improve the reliability and efficiency of transportation networks in our cities. References 1 Susilawati S, Taylor MAP, Somenahalli SVC. Distributions of travel time variability on urban roads. Journal of Advanced Transportation 2013; 47(8): 720– 736. Wiley Online LibraryWeb of Science®Google Scholar 2 Wang X, Khattak A. Role of travel information in supporting travel decision adaption: exploring spatial patterns. Transportmetrica A – Transport Science 2013; 9(4): 316– 334. CrossrefWeb of Science®Google Scholar 3 Bekhor S, Cohen Y Solomon C. Evaluating long-distance travel patterns in Israel by tracking cellular phone positions. Journal of Advanced Transportation 2013; 47(4): 435– 446. Wiley Online LibraryWeb of Science®Google Scholar 4 Farooq U, Siddiqui MA, Gao L Hardy JL. Intelligent transportation systems: an impact analysis for Michigan. Journal of Advanced Transportation 2012; 46(1): 12– 25. Wiley Online LibraryWeb of Science®Google Scholar 5 Ouyang Y. Experimental study on using advance demand information to mitigate the bullwhip effect via decentralised negotiations. Transportmetrica B-Transport Dynamics 2014; 2(3): 169– 187. CrossrefWeb of Science®Google Scholar 6 Feng Y, Sun J Chen P. Vehicle trajectory reconstruction using automatic vehicle identification and traffic count data. Journal of Advanced Transportation 2014. DOI:10.1002/atr.1260. Wiley Online LibraryGoogle Scholar 7 Yang X, Lu YC, C GL. Exploratory analysis of an optimal variable speed control system for a recurrently congested freeway bottleneck. Journal of Advanced Transportation 2014; DOI: 10.1002/atr.1285Wiley Online LibraryGoogle Scholar 8 Lu Z, Rao W, Wu YJ, Guo L Xia J. A Kalman filter approach to dynamic OD flow estimation for urban road networks using multi-sensor data. Journal of Advanced Transportation 2014. DOI:10.1002/atr.1292. Wiley Online LibraryGoogle Scholar 9 Zhang L, Lou Y. Coordination of semi-actuated signals on arterials. Journal of Advanced Transportation 2013. DOI:10.1002/atr.1259. Wiley Online LibraryGoogle Scholar 10 Zhu F, Ukkusuri SV. A reinforcement learning approach for distance-based dynamic tolling in the stochastic network environment. Journal of Advanced Transportation 2014. DOI:10.1002/atr.1276. Wiley Online LibraryGoogle Scholar 11 Gan H, Ye X. Whether to enter expressway or not? The impact of new variable message sign information. Journal of Advanced Transportation 2014. DOI:10.1002/atr.1273. Wiley Online LibraryGoogle Scholar 12 Wang H, Li Z, Hurwitz D Shi J. Parametric modeling of the heteroscedastic traffic speed variance from loop detector data. Journal of Advanced Transportation 2013. DOI:10.1002/atr.1258. Wiley Online LibraryGoogle Scholar Volume49, Issue2Special Issue: Emerging Technologies for Intelligent TransportationMarch 2015Pages 171-173 ReferencesRelatedInformation