System-Level Optimization of Multi-Modal Transportation Networks for Energy Efficiency using Personalized Incentives: Formulation, Implementation, and Performance

Abstract

The paper presents the system optimization (SO) framework of Tripod, an integrated bi-level transportation management system aimed at maximizing energy savings of the multi-modal transportation system. From the user’s perspective, Tripod is a smartphone app, accessed before performing trips. The app proposes a series of alternatives, consisting of a combination of departure time, mode, and route. Each alternative is rewarded with an amount of tokens which the user can later redeem for goods or services. The role of SO is to compute the optimized set of tokens associated with the available alternatives to minimize the system-wide energy consumption under a limited token budget. To do so, the alternatives that guarantee the largest energy reduction must be rewarded with more tokens. SO is multi-modal, in that it considers private cars, public transit, walking, car pooling, and so forth. Moreover, it is dynamic, predictive, and personalized: the same alternative is rewarded differently, depending on the current and the predicted future condition of the network and on the individual profile. The paper presents a method to solve this complex optimization problem and describe the system architecture, the multi-modal simulation-based optimization model, and the heuristic method for the online computation of the optimized token allocation. Finally it showcases the framework with simulation results.

Game theoretic approach for public multi‐mode transportation in smart cities

“…Fourth (constraints 22-24), the time the vehicle takes T V v;p ðx; sv drop v;m Þ, whether it is the public bus, the car ride-sharing, or the bicycle, to reach the next stop, must be equal to or less than the time taken for the other vehicle to travel from its current location to the passenger's pickup station T B b;p ðx; sb pick i;p Þ or T C c;p ðx; sc pick j;p Þ or T R r;p ðx; sr pick k;p Þ). Fifth (constraints [25][26][27], the time that the vehicle takes to p Þ is the time from the current location of the bus, the car ride-sharing, and the bicycle, respectively, to the pickup station. Sixth (constraints 28-33), the total time or price for the passenger p when he takes any type of transportation must be less than or equal to the threshold value (T max and P max ).…”

SMART mobility via prediction, optimization and personalization

“…Tripod's overall system-wide maximization of energy efficiency is achieved through a bilevel optimization approach with the system optimization as strategy (top level) and the app menu generation as the personalization (lower level). The link between these two loosely coupled problems is achieved through the real-time computation of the token energy efficiency (TEE), defined as the amount of energy a traveler must save to earn one token (Araldo et al, 2019). The TEE is the key decision variable of the system optimization and is used in every menu personalization, then triggered by each trip request from a control point traveler, i.e., Tripod user.…”

“…The Tripod app also keeps track of Tripod users' preferences from their menu selections and informs the system optimization for better predictions. As previously mentioned, the response to different TEE and overall predicted demand is embedded in the prediction framework (for more information the reader is referred to Araldo et al, 2019).…”

“…A reference energy value is computed for each trip request based on the expected energy consumption without tokens. The reader is referred to Lima Azevedo et al ( 2018) and Araldo et al (2019) for details on the formulation, implementation, and configuration of Tripod.…”

Analysis and Design of Markets for Tradable MobilityCredit Schemes

“…The second component of Trinity is the bi-level optimization module, which is responsible for setting the token charges or tariff for each travel alternative in real time ('system-level' optimization) and providing personalized 'user-optimal' menu's to travelers ('user-level' optimization). The system-level optimization utilizes a simulation-based predictive system that uses real-time data from the market and from sensors in the transportation system (Araldo et al, 2019). The overall policy objectives for Trinity in terms of congestion, emissions, network performance, quality of service and sustainability is defined via the system-level optimization.…”