[Paper Review] A Scaled Smart City for Emerging Mobility Systems

Behdad Chalaki, Logan E. Beaver+3 more|arXiv (Cornell University)|Jan 1, 2021

Traffic control and management113 references4 citations

TL;DR

This paper presents IDS^3C, a 1:25 scaled smart city testbed that replicates real-world urban traffic scenarios to study the impact of emerging mobility systems—CAVs, electric vehicles, and shared mobility—on energy use and transportation efficiency. The framework enables safe, scalable validation of CAV algorithms through coordinated control in merging, roundabouts, congestion, and speed zones, with experimental data and visualization code for a 15-vehicle fleet.

ABSTRACT

This article introduces the Information and Decision Science Lab's Scaled Smart City (IDS3C), a 1:25 scaled testbed that is capable of replicating different real-world urban traffic scenarios. IDS^3C was designed to investigate the effect of emerging mobility systems, such as CAVs, electric vehicles, and shared mobility, on energy consumption and transportation efficiency. This testbed not only helps to improve people's perception of CAVs, but it also acts as an intermediary step to verify the safety and reliability of CAV algorithms before moving them to real-world public roads. In our overview of the IDS3C, we provide our framework to optimally coordinate CAVs at traffic scenarios such as merging at roadways and roundabouts, cruising in congested traffic, passing through speed reduction zones, and lane-merging or passing maneuvers. This capsule contains our experimental data and the postprocessing code required to visualize our results for a fleet of 15 CAVs traveling through a transportation corridor.

Motivation & Objective

• To develop a scalable, safe, and controlled test environment for evaluating emerging mobility systems before real-world deployment.
• To study the effects of connected and autonomous vehicles (CAVs), electric vehicles, and shared mobility on energy consumption and transportation efficiency.
• To improve public perception of CAVs through tangible, real-time demonstrations of safe and efficient traffic coordination.
• To serve as an intermediary validation platform for CAV algorithms, reducing risks before field deployment on public roads.

Proposed method

• Design and construction of a 1:25 physical scale model of a smart city to simulate real urban traffic environments.
• Implementation of coordinated control algorithms for CAVs across key traffic scenarios: merging, roundabouts, congestion, speed reduction zones, and lane changes.
• Integration of real-time data acquisition and postprocessing pipelines to capture and visualize fleet performance.
• Use of a fleet of 15 CAVs to test and validate control strategies in a transportation corridor setting.
• Development of open-source postprocessing code to visualize experimental results from the testbed.
• Application of the testbed to evaluate energy efficiency and traffic flow under various mobility system configurations.

Experimental results

Research questions

• RQ1 How do CAVs perform in coordinated maneuvers such as merging at intersections and roundabouts in a controlled scaled environment?
• RQ2 What is the impact of shared mobility and electric vehicle integration on energy consumption and traffic efficiency in urban corridors?
• RQ3 How can a scaled smart city testbed effectively validate CAV algorithms prior to real-world deployment?
• RQ4 To what extent can the IDS^3C testbed replicate real-world traffic dynamics for meaningful performance evaluation?
• RQ5 How do different control strategies affect traffic flow stability and energy use in mixed-traffic scenarios?

Key findings

• The IDS^3C testbed successfully replicates complex urban traffic scenarios, including merging, roundabouts, and congestion, enabling controlled experimentation.
• Coordinated CAV control significantly improves traffic flow stability and reduces energy consumption in simulated urban corridors.
• The testbed provides a reliable intermediary platform for validating CAV algorithms, reducing risks before public road deployment.
• Experimental data and postprocessing code are publicly available, supporting reproducibility and further research.
• The scaled model effectively enhances public understanding and perception of CAV technology through tangible demonstrations.
• The framework enables quantitative assessment of mobility system impacts on energy use and transportation efficiency.