Route choice is often assessed with either a modeling technique or field observations. Field observations have historically used a variation of license plate matching. The proposed technique assesses route choice and travel time that uses an anonymous Bluetooth media access control (MAC) address sampling technique as a surrogate for license plate matching to assess route choice. The Bluetooth sampling technique was used to evaluate the impact of an unexpected bridge closure in northwest Indiana, including an assessment of the proportion of vehicles using each of four alternate routes. The Bluetooth technology also provided a means to collect travel time data for each alternate route; these observed travel times were also compared with travel time estimates obtained by route classification and link distance. In general, the route choice behavior was consistent with observed travel time estimates. The Bluetooth sampling technique is cost-effective to deploy, and although results are approximate, direct measurement of travel times and route choice is useful for public agencies to assess mobility and travel time reliability along alternate routes.

Extensive literature in the adaptive control field uses local detection available from the traffic controller as input to various control models to adjust splits, cycle lengths, and offsets. All these models have implicit control objectives, which include facilitated progression, minimized stops, minimized delay, and equitable allocation of green time. Enormous opportunities exist to incorporate probe data into the decision process with respect to when and where adaptive control can be used and which operating objectives are most applicable to a corridor as well as to an outcome assessment tool to evaluate the effectiveness of adaptive control. The research reported in this paper compared how probe data sources could be used to identify appropriate adaptive control objectives and to assess the performance of adaptive systems. Four case studies demonstrated how travel time data could be used to evaluate existing conditions, to evaluate the outcome of a traditional signal retiming, and to assess the feasibility of adaptive control opportunities. Currently, the richest probe data sets are provided by agency-installed equipment. Given the increasing penetration of crowd-sourced probe data devices and the onset of connected vehicle infrastructure, however, these sources could provide similarly rich data. This paper recommends that commercial data providers begin to develop more detailed base maps. These maps would provide richer probe data information, such as hour-by-hour statistical distributions and approach delay for signalized arterials for which the segments did not span multiple intersections. This recommendation should motivate agencies to develop more detailed specifications for probe data that will better serve their needs.

Microsimulation modeling is one of the contemporary techniques that has potential to perform complex transportation studies faster, safer, and in a less expensive manner. However, to get accurate and reliable results, the microsimulation models need to be well calibrated. Microsimulation model consists of various sub-models each having many parameters, most of which are user-adjustable and are attuned for calibrating the model. Manual calibration involves an iterative trial-and-error process of using the intuitive discrete values of each parameter and feasible combinations of multiple parameters each time until the desired results are obtained. With this approach, it is possible to easily get caught in a never-ending circular process of fixing one problem only to generate another. This can make manual calibration a time-consuming process and is suggested only when the number of parameters is small. However, when the calibration parameter subset is large, an automated process is suggested in the literature. Amongst the meta-heuristics used for calibrating microsimulation models, the genetic algorithm (GA) has been widely used and simulated annealing (SA) has been used only once in the past. Thus, the question of which meta-heuristics is more suitable for the problem of calibration of the microsimulation model still remains open. Thus, the objective of this paper is to evaluate and compare the manual and three (the GA, SA, and tabu search (TS)) meta-heuristics for calibration of microsimulation models. This paper therefore addresses the need to examine and identify the suitability of a meta-heuristics for calibrating microsimulation models. The results show that the meta-heuristics approach can be relied upon for calibrating simulation models very effectively, as it offers the benefit of automating the cumbersome calibrating process. All three meta-heuristics (the GA, SA, and TS) have the ability to find better calibrating parameters than the manually calibrated parameters. The number of better solutions, the best solution, and convergence to the best solution by TS is better than those by the GA and SA. Significant time can be saved by automating calibration of microsimulation models using meta-heuristics. The approach presented in this research can be used to help engineers and planners achieve better modeled results, as the calibration of microsimulation models is likely to become more complex in the future.