Most current Travel Demand Management (TDM) programs such as vanpooling, ridesharing, or transit focus on managing travel demand of specific groups of commuters but are limited in effectively managing demand for automobile drivers, who are unable or unwilling to participate in such programs. This paper highlights results from a pilot field study conducted in a large west coast city experiencing major traffic congestion, and documents results of the use of an incentive-based active demand management (ADM) system focusing on automobile commuters. The system, called “Metropia,” predicts future traffic conditions, applies a proprietary routing algorithm to find time-dependent shortest paths for different departure times, and, based on user request, provides automobile travelers with multiple departure times and route choices. Each of these travel choices are assigned points values, with higher points (and thus more valuable rewards) available for travelling during off-peak times and less congested routes, and...

With the development of smartphones and portable GPS devices, large-scale, high-resolution GPS data can be collected. Map matching is a critical step in studying vehicle driving activity and recognizing network traffic conditions from the data. A new trajectory segmentation map-matching algorithm is proposed to deal accurately and efficiently with large-scale, high-resolution GPS trajectory data. The new algorithm separated the GPS trajectory into segments. It found the shortest path for each segment in a scientific manner and ultimately generated a best-matched path for the entire trajectory. The similarity of a trajectory segment and its matched path is described by a similarity score system based on the longest common subsequence. The numerical experiment indicated that the proposed map-matching algorithm was very promising in relation to accuracy and computational efficiency. Large-scale data set applications verified that the proposed method is robust and capable of dealing with real-world, large-scale GPS data in a computationally efficient and accurate manner.

Deploying shared automated electric vehicles (SAEVs) on current roadways in cities will significantly shape current transportation systems and make our urban mobility systems more efficient, convenient, and environmentally friendly. Utilizing wireless power transfer (WPT) technology to charge the SAEVs provides perfect fits for realizing a fully automated mobility system. However, the investment in wireless charging infrastructure (WCI) presents a critical barrier for commercializing and adopting this technology. The barrier can be cleared by realizing the proper design of the WPT system that maximizes the benefits and minimizes the cost of WCI at the same time. This paper introduces a system design optimization tool and methodology for WCI for serving fixed-route SAEVs in automated mobility districts (AMDs). The tool offers the capability of integrating driving data (simulated or collected from the real world), vehicle parameters (e.g., battery, motor, dimensions, and so on), and wireless charger characteristics (rate, locations, alignment, and so on) to generate energy and state-of-charge profiles for each vehicle, considering motoring, regenerative braking, and charging. Furthermore, the proposed tool incorporates a multi-objective optimization layer for searching the optimum design parameters based on predefined objectives and constraints. The proposed method was utilized to design the WCI for a hypothetical AMD scenario with four SAEVs. The outcomes show that implementing in-route wireless chargers at designated stops for the SAEVs with maximum power level has the potential to provide a charge sustaining operation with 52% reduction in the on-board battery and presents the most cost-effective solution. The proposed solution is assessed in comparison with other charging technologies, such as stationary WPT and dc fast charging, and it shows the most feasible option for an AMD network in terms of cost, convenience, and performance.

With the development of and advances in smartphones and global positioning system (GPS) devices, travelers’ long-term travel behaviors are not impossible to obtain. This study investigates the pattern of individual travel behavior and its correlation with social-demographic features. For different social-demographic groups (e.g., full-time employees and students), the individual travel behavior may have specific temporal-spatial-mobile constraints. The study first extracts the home-based tours, including Home-to-Home and Home-to-Non-Home, from long-term raw GPS data. The travel behavior pattern is then delineated by home-based tour features, such as departure time, destination location entropy, travel time, and driving time ratio. The travel behavior variability describes the variances of travelers’ activity behavior features for an extended period. After that, the variability pattern of an individual’s travel behavior is used for estimating the individual’s social-demographic information, such as social-demographic role, by a supervised learning approach, support vector machine. In this study, a long-term (18-month) recorded GPS data set from Puget Sound Regional Council is used. The experiment’s result is very promising. The sensitivity analysis shows that as the number of tours thresholds increases, the variability of most travel behavior features converges, while the prediction performance may not change for the fixed test data.