As of 2014, 54% of the earth’s population resides in urban areas, a percentage expected to reach 66% by 2050. This increase would amount to 2.5B people added to urban populations. At the same time, there are now 28 mega-cities (with 10M people) worldwide, accounting for 22% of the world’s urban population, and projections indicate more than 41 mega-cities by 2030. It stands to reason that the management and sustainability of urban areas has become one of the most critical challenges our societies face today.
Read an Introductory Article about Smart Cities or presentation
There are several projects in our research group addressing issues in Smart Cities which we often view as an instance of a complex stochastic Cyber-Physical System (CPS).
Bike Sharing Systems
We solved the problem of load balancing of bike stations assuming finite-capacity. We use Receding Horizon Control to optimally route a fleet of bike replenishment trucks over a planning horizon. The event-driven controller of this strategy decreases the complexity of finding optimal routes so that it may be used in real-time. Through simulation experiments, we found that a planning horizon of around 30 minutes is enough to be close to the full scheduling optimal solution.
- The assignment of passengers to a fleet of vehicles in Ride Sharing System (RSS) is an exiting Combinatorial Problem. The problem involves a real-time optimization of assigning incoming passengers to vehicles as well as defining the routing strategy for each fleet’s vehicle. As available information, it considers the origin-destination location of passengers and, the capacity and position of vehicles. We tackle this problem using a Receding Horizon Control (RHC) strategy. This event-driven online model helps to reduce the complexity of the problem by defining a limited event-horizon to search.
- Analyzing traffic data is becoming fundamental for making smarter decisions. Such data can be seen as enablers in making inferences, identifying traffic congestion bottlenecks, guiding individual driver routing decisions, and informing interventions by municipal and state authorities. The ultimate goal is to reduce congestion and its effects, including harmful emissions. We created Congestion Maps, a web-based inference engine for visualizing and analyzing congestion and travel times in the Eastern Massachusetts (EMA) road network.
Social Routing and the Price of Anarchy
We study the problem of routing Connected and Automated Vehicles (CAVs) in the presence of mixed traffic (coexistence of regular vehicles and CAVs). In this setting, we assume that all CAVs belong to the same fleet, and can be routed using a centralized controller. The routing objective is to minimize a given overall fleet traveling cost (travel time or energy consumption). We assume that regular vehicles (non-CAVs) choose their routing decisions selfishly to minimize their traveling time. We propose an algorithm that deals with the routing interaction between CAVs and regular uncontrolled vehicles.
The results suggest that collaborative routing decisions of CAVs improve not only the cost of CAVs, but also that of the non-CAVs. Furthermore, even a small CAV penetration rate can ease congestion for the entire network.
Dukietown Smart Megacity Infrastructure
- Powered by Linux, Docker, and ROS, our current Duckietown infrastructure (Mega-city environment) simulates traffic in a Smart City. Robots (cars) are controlled in order to run autonomously and to communicate with the transportation system (vehicle to vehicle, vehicle to infrastructure). Traffic lights are installed at selected intersections and its red/green cycles can be remotely controlled using real-time traffic information of the system. The objective is to reduce the energy consumption of cars at intersections caused by unnecessary stops.
Robotic Urban-Like Environment (RULE)
- RULE is an experimental platform for automatic deployment of robotic cars in an urban-like environment. The cars are Khepera II and Khepera III miniature robots with processing, sensing, and communication capabilities. The “city” has streets, intersections, traffic lights, and parking spots, and can be easily reconfigured. Four overhead cameras, serving as a GPS, are used to produce a topological map of the environment.
- Studies have estimated that on a daily basis 30% of traffic in the downtown area of major cities is due to cruising for parking spots. In addition to aggravation and the waste of time and fuel for drivers looking for parking, this also contributes to the additional waste of time and fuel for other drivers as a result of traffic congestion.
- Street Bump is a crowd-sourcing project belonging to the Smart City, which aims to help residents improve their neighborhood streets.
Optimal Merging Control for CAVs
- This research is about the optimal control of Connected and Automated Vehicles (CAVs) arriving from two curved roads at a merging point where the objective is to jointly minimize the travel time, energy consumption, and the passenger discomfort of each CAV. The solution guarantees that a speed-dependent safety constraint and a lateral rollover avoidance constraint are always satisfied, both at the merging point and everywhere within a control zone which precedes it. Read more here.
See more here
Huile Xu, Wei Xiao, Christos G Cassandras, Yi Zhang, Li Li. “A General Framework for Decentralized Safe Optimal Control of Connected and Automated Vehicles in Multi-Lane Intersections”. Submitted to IEEE Transactions on Intelligent Transportation Systems (2019).
Wei Xiao and Christos G. Cassandras. Decentralized optimal merging control for connected and automated vehicles with safety constraints guarantees. Automatica, Volume 123, 109333, 2021.