Ouyang, Talebpour optimize next-generation transportation systems

Next-generation technologies, such as electric, connected and autonomous vehicles, will improve the safety and efficiency of our transportation network.

Two Illinois Center for Transportation researchers are optimizing the rollout of next-generation vehicles in projects sponsored by the Center for Connected and Automated Transportation.

Shared Mobility for Passengers and Freight: Modularized Vehicle Platforms

 Advancements in transportation technologies have led to increased use of autonomous taxis and freight vehicles such as drones and delivery robots. 

One such advancement are autonomous modularized vehicle platforms — also known as modular chassis, which can carry multiple customizable cabins that typically serve distinct purposes such as passenger or freight delivery.

Yanfeng Ouyang, George Krambles Endowed Professor and ICT associate director for mobility, aims to optimize shared passenger and freight services using modularized vehicle platforms along with CEE graduate students Shiyu Shen and Yuhui Zhai.

Modularized vehicle platforms allow providers of services such as ride-hailing or package delivery, which are traditionally serviced by separate fleets, to serve multiple types of customers using the same pool of vehicles.

Service providers may load passenger or freight cabins interchangeably onto a modular chassis, or core structure, as needed to help meet real-time demands for passengers (which often arise in rush hours) or freight deliveries (which are often planned during off-peak hours).

Key questions Ouyang’s team sought to answer in the CCAT project were how to best match multiple types of customers with suitable service vehicles in real time as well as how to optimize the efficiency of the integrated system by swapping cabins at the right location and the right time.

Ouyang’s team built models to estimate the system’s expected operational performance as well as conducted a series of simulations to verify those models.

Their findings are then used to design planning policy and operational strategies to enhance the performance of modularized vehicle platforms, such as whether newly arriving customers and idle vehicles should be instantly matched or swapped or pooled into a batch for matching.

“We show that by sharing resources between two traditionally separate mobility modes and operating them optimally, overall system performance can be significantly improved while meeting the overall mobility needs,” Ouyang said.

“This line of research can help operators and policymakers plan and operate mobility-as-a-service for both passengers and freight,” he added.

Quantum Optimization: Long-Distance Charging Infrastructure

Electric vehicle adoption rates are increasing, but a significant barrier remains to widespread adoption: inadequate charging infrastructure for long-distance travel.

Alireza Talebpour, CEE professor and ICT mobility autonomy lead, is seeking to facilitate long-distance travel by determining optimum locations for electric vehicle charging stations.

Talebpour, along with CEE graduate student Tina Radvand, leverages quantum computing in the CCAT project to determine charging stations’ optimum locations.

Other computational means, such as using exact methods or heuristic and metaheuristic approaches, are computationally demanding or have difficulty guaranteeing optimal solutions. Quantum computing overcomes these limitations, allowing researchers to explore multiple solutions simultaneously.

“Transportation problems often involve complex networks that require optimization for efficiency,” Talebpour said. “This research demonstrates how quantum computing can be leveraged to optimize these networks in ways that classical algorithms cannot achieve, offering the potential for significant improvements in areas like route planning, infrastructure placement and overall system efficiency.”

Talebpour’s team introduced a quantum optimization algorithm that determines the ideal charging station locations needed to accommodate all possible EV trips while minimizing the total number of stations needed.

They tested their algorithm by determining the optimal locations of EV charging stations in central Illinois.

Their results indicate the developed approach ensures that all vehicles can complete round trips without battery depletion.