Metropia Massive Mobility Management (M4)
Special event traveler cooperate to save travel time for self and others
Massive demand surges related to nonrecurring events (e.g., long-weekend holidays and special events) can clog the entire traffic system, resulting in prolonged travel time for individual travelers, and severe congestion for the entire transportation system. Traditional traffic control and intelligent transportation systems (ITS) strategies have shown to be of limited effect when demand exceeds system capacity by multiple folds. To this end, Metropia has recently developed a Massive Mobility Management (M4) system to solve for the optimal departure time recommendations for each of the individual participants, forming the Cooperative Demand Management (CDM) concept and strategy.
Metropia Massive Mobility Management
The Metropia Massive Mobility Management (M4) system implements CDM by integrating a user app, a traffic simulator, the M4 Modeler, and an optimization solver, the M4 solver, as shown in the image below.
The M4 system starts by soliciting input from users (either via a web page or the app) days to hours before the start of any nonrecurring events. The users are asked to provide their origin, destination, and multiple feasible departure time windows to the system (e.g., Friday early morning, late morning, or Saturday early afternoon, etc.). The system collects this information from hundreds of thousands or even millions of users and creates a time-varying demand scenario for the period of interest. The system then loads this time-varying demand into the M4 Modeler.
The M4 Modeler is a large-scale mesoscopic simulation model calibrated for local capability conditions and is capable of reporting performance outcomes when given time-varying demand for the traffic system. The DynusT model, which is a DTA model developed by Metropia, is utilized in the M4 system. The M4 Modeler feeds its outputs to the M4 Solver, which then determines the optimal departure time for each of the users..
Once a solution is obtained, the system communicates with users and informs them of the best time to leave. The initial projections remain tentative for specific users. The system will also stay in contact for final confirmation before their departure. The users are asked to confirm or decline the recommendation.
To make these M4 strategies effective, the M4 computational framework must be capable of operating in real-time such that the solution can be re-optimized if significant deviations from the original prediction arise. This can be due to various exogenous factors such as weather, incidents, and inaccurate estimation of participation during either the planning, optimization or real-time phases. M4 utilizes Rolling Horizon Transportation Management to conduct the re-optimization process to keep the recommendations close to real-time traffic conditions, as shown in the image below.
An evaluation of the M4 system was performed on Taiwan's national highway network. The results indicate that the M4 system reduced the average travel time of the program participants by 17%~29%. The following steps describe the evaluation process.
In order to understand travelers' willingness to participate in the CDM program, a questionnaire was sent to target users via text. The target users were identified as potential national highway system users during the specific holiday. The questions included trip purpose, departure time adjustment willingness, and the influence of incentives. This survey information provided basic knowledge we utilized to design scenarios used to evaluate participants' departure time adjustment range.
Next, the M4 Modeler was developed based on the DynusT model and Taiwan's national highway network. To realize real travel demand, Metropia was provided with the time-dependent origin-destination demand matrices calculated based on the target users' phone signal data provided by the local telecom company.
Finally, the M4 solver determined each program participant's optimal departure time while considering different levels of user participation and their departure time adjustment range from their survey response. The M4 solver set the minimum average travel time as the optimal solution to determine each user's departure time and corresponding incentive.
This evaluation result provides a promising conclusion that Metropia's M4 system are worth implementing for travel management during long-holiday events. The key highlights are summarized below:
The survey results show that 81% of total respondents were willing to coordinate their departure times in order to reduce everyone’s travel time. This means that the respondents who have consistently suffered in the past have a rather significant motivation trigger for them to participate in a more cooperative scheme (instead of a competitive one) in order to solve the problem together.
The M4 system developed a DTA model built with DynusT to simulate Taiwan’s national highway system during a long holiday. The simulation results were compared with traffic data from the electronic toll collection system. The comparison results indicate that the mean absolute percentage error (MAPE) of the traffic volume, travel time, and travel speed was 25%.
The M4 system examined different levels of user participation and various ranges of departure time adjustment willingness for the selected participants. The average travel time was reduced by 17%-29% when 20% of the total travelers participated in the CDM program and were willing to change their departure time by 4 to 8 hours.
Metropia Massive Mobility Management (M4) is an innovative technology that integrates a user app, travel behavior analysis, and a DTA model. The M4 system has the capability to reduce congested traffic during long holidays and massive demand events, such as ball games and concerts.