Understanding and Developing Equitable and Fair Transportation Systems
- URL: http://arxiv.org/abs/2209.10589v1
- Date: Wed, 21 Sep 2022 18:26:19 GMT
- Title: Understanding and Developing Equitable and Fair Transportation Systems
- Authors: Weizi Li
- Abstract summary: We need to better design and plan our transportation system.
Roads and bridges are found to better connect affluent sectors while excluding the poor.
This proposal is the first step toward answering these questions.
- Score: 3.1473798197405944
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The transportation system is an interplay between infrastructure, vehicles,
and policy. During the past century, the rapid expansion of the road network,
blended with increasing vehicle production and mobility demands, has been
stressing the system's capacity and resulting in a shocking amount of annual
costs. To alleviate these costs while providing passengers with safe and
efficient travel experiences, we need to better design and plan our
transportation system. To start with, not only the design of our road network
is topologically flawed but also our infrastructure likely facilitates
inequality: roads and bridges are found to better connect affluent sectors
while excluding the poor. While technological advancements such as connected
and autonomous vehicles (CAVs) and novel operation modes such as shared economy
have offered new opportunities, questions remain. First, what is the
relationship between the road network, community development, demographics, and
mobility behaviors? Second, by leveraging the insights from studying the first
question, can we better plan, coordinate, and optimize vehicles in different
modalities such as human-driven and autonomous to construct safe, efficient,
and resilient traffic flows? Third, how can we build an intelligent
transportation system to promote equity and fairness in our community
development? This proposal is the first step toward answering these questions.
Related papers
- Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots [50.02055068660255]
Navigating urban environments poses unique challenges for robots, necessitating innovative solutions for locomotion and navigation.
This work introduces a fully integrated system comprising adaptive locomotion control, mobility-aware local navigation planning, and large-scale path planning within the city.
Using model-free reinforcement learning (RL) techniques and privileged learning, we develop a versatile locomotion controller.
Our controllers are integrated into a large-scale urban navigation system and validated by autonomous, kilometer-scale navigation missions conducted in Zurich, Switzerland, and Seville, Spain.
arXiv Detail & Related papers (2024-05-03T00:29:20Z) - Trip Planning for Autonomous Vehicles with Wireless Data Transfer Needs
Using Reinforcement Learning [7.23389716633927]
We propose a novel reinforcement learning solution that prioritizes high bandwidth roads to meet a vehicles data transfer requirement.
We compare this approach to traffic-unaware and bandwidth-unaware baselines to show how much better it performs under heterogeneous traffic.
arXiv Detail & Related papers (2023-09-21T23:19:16Z) - Improving Urban Mobility: using artificial intelligence and new
technologies to connect supply and demand [7.347028791196305]
The are of intelligent transportation systems (ITS) aims at investigating how to employ information and communication technologies to problems related to transportation.
In this panorama, artificial intelligence plays an important role, especially with the advances in machine learning.
arXiv Detail & Related papers (2022-03-18T14:37:33Z) - Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers [126.81938540470847]
We propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories.
In this work, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene.
We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.
arXiv Detail & Related papers (2021-06-22T15:40:21Z) - Incentivizing Efficient Equilibria in Traffic Networks with Mixed
Autonomy [17.513581783749707]
Vehicle platooning can potentially reduce traffic congestion by increasing road capacity via vehicle platooning.
We consider a network of parallel roads with two modes of transportation: (i) human drivers, who will choose the quickest route available to them, and (ii) a ride hailing service, which provides an array of autonomous vehicle route options, each with different prices, to users.
We formalize a model of vehicle flow in mixed autonomy and a model of how autonomous service users make choices between routes with different prices and latencies.
arXiv Detail & Related papers (2021-05-06T03:01:46Z) - End-to-End Intersection Handling using Multi-Agent Deep Reinforcement
Learning [63.56464608571663]
Navigating through intersections is one of the main challenging tasks for an autonomous vehicle.
In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided.
We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
arXiv Detail & Related papers (2021-04-28T07:54:40Z) - AI in Smart Cities: Challenges and approaches to enable road vehicle
automation and smart traffic control [56.73750387509709]
SCC ideates on a data-centered society aiming at improving efficiency by automating and optimizing activities and utilities.
This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control.
arXiv Detail & Related papers (2021-04-07T14:31:08Z) - Emergent Road Rules In Multi-Agent Driving Environments [84.82583370858391]
We analyze what ingredients in driving environments cause the emergence of road rules.
We find that two crucial factors are noisy perception and agents' spatial density.
Our results add empirical support for the social road rules that countries worldwide have agreed on for safe, efficient driving.
arXiv Detail & Related papers (2020-11-21T09:43:50Z) - PassGoodPool: Joint Passengers and Goods Fleet Management with
Reinforcement Learning aided Pricing, Matching, and Route Planning [29.73314892749729]
We present a demand aware fleet management framework for combined goods and passenger transportation.
Our proposed model is deployable independently within each vehicle as this minimizes computational costs associated with the growth of distributed systems.
arXiv Detail & Related papers (2020-11-17T23:15:03Z) - Smart Urban Mobility: When Mobility Systems Meet Smart Data [55.456196356335745]
Cities around the world are expanding dramatically, with urban population growth reaching nearly 2.5 billion people in urban areas and road traffic growth exceeding 1.2 billion cars by 2050.
The economic contribution of the transport sector represents 5% of the GDP in Europe and costs an average of US $482.05 billion in the U.S.
arXiv Detail & Related papers (2020-05-09T13:53:01Z) - Autonomous Shuttle-as-a-Service (ASaaS): Challenges, Opportunities, and
Social Implications [10.075017640104843]
Smart mobility systems aim to support efficient exploitation of city transport facilities.
In the last few years, several cities indicated interest in using Autonomous Vehicles for the "last-mile" mobility services.
Autonomous Shuttles (AS) are beginning to be thought of as a new mobility/delivery service into the city center.
arXiv Detail & Related papers (2020-01-14T12:34:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.