Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting

Modern urbanization is demanding smarter technologies to improve a variety of applications in intelligent transportation systems to relieve the increasing amount of vehicular traffic congestion and incidents. Existing incident detection techniques are limited to the use of sensors in the transportation network and hang on human-inputs. Despite of its data abundance, social media is not well-exploited in such context. In this paper, we develop an automated traffic alert system based on Natural Language Processing (NLP) that filters this flood of information and extract important traffic-related bullets. To this end, we employ the fine-tuning Bidirectional Encoder Representations from Transformers (BERT) language embedding model to filter the related traffic information from social media. Then, we apply a question-answering model to extract necessary information characterizing the report event such as its exact location, occurrence time, and nature of the events. We demonstrate the adopted NLP approaches outperform other existing approach and, after effectively training them, we focus on real-world situation and show how the developed approach can, in real-time, extract traffic-related information and automatically convert them into alerts for navigation assistance applications such as navigation apps.Comment: This paper is accepted for publication in IEEE Technology Engineering Management Society International Conference (TEMSCON'20), Metro Detroit, Michigan (USA

Route Planning in Transportation Networks

We survey recent advances in algorithms for route planning in transportation networks. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale. A variety of techniques provide different trade-offs between preprocessing effort, space requirements, and query time. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. Journey planning on public transportation systems, although conceptually similar, is a significantly harder problem due to its inherent time-dependent and multicriteria nature. Although exact algorithms are fast enough for interactive queries on metropolitan transit systems, dealing with continent-sized instances requires simplifications or heavy preprocessing. The multimodal route planning problem, which seeks journeys combining schedule-based transportation (buses, trains) with unrestricted modes (walking, driving), is even harder, relying on approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4, previously published by Microsoft Research. This work was mostly done while the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at Microsoft Research Silicon Valle

TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Models

With the promotion of chatgpt to the public, Large language models indeed showcase remarkable common sense, reasoning, and planning skills, frequently providing insightful guidance. These capabilities hold significant promise for their application in urban traffic management and control. However, LLMs struggle with addressing traffic issues, especially processing numerical data and interacting with simulations, limiting their potential in solving traffic-related challenges. In parallel, specialized traffic foundation models exist but are typically designed for specific tasks with limited input-output interactions. Combining these models with LLMs presents an opportunity to enhance their capacity for tackling complex traffic-related problems and providing insightful suggestions. To bridge this gap, we present TrafficGPT, a fusion of ChatGPT and traffic foundation models. This integration yields the following key enhancements: 1) empowering ChatGPT with the capacity to view, analyze, process traffic data, and provide insightful decision support for urban transportation system management; 2) facilitating the intelligent deconstruction of broad and complex tasks and sequential utilization of traffic foundation models for their gradual completion; 3) aiding human decision-making in traffic control through natural language dialogues; and 4) enabling interactive feedback and solicitation of revised outcomes. By seamlessly intertwining large language model and traffic expertise, TrafficGPT not only advances traffic management but also offers a novel approach to leveraging AI capabilities in this domain. The TrafficGPT demo can be found in https://github.com/lijlansg/TrafficGPT.git

Carbon Free Boston: Social equity report 2019

OVERVIEW: In January 2019, the Boston Green Ribbon Commission released its Carbon Free Boston: Summary Report, identifying potential options for the City of Boston to meet its goal of becoming carbon neutral by 2050. The report found that reaching carbon neutrality by 2050 requires three mutually-reinforcing strategies in key sectors: 1) deepen energy efficiency while reducing energy demand, 2) electrify activity to the fullest practical extent, and 3) use fuels and electricity that are 100 percent free of greenhouse gases (GHGs). The Summary Report detailed the ways in which these technical strategies will transform Boston’s physical infrastructure, including its buildings, energy supply, transportation, and waste management systems. The Summary Report also highlighted that it is how these strategies are designed and implemented that matter most in ensuring an effective and equitable transition to carbon neutrality. Equity concerns exist for every option the City has to reduce GHG emissions. The services provided by each sector are not experienced equally across Boston’s communities. Low-income families and families of color are more likely to live in residences that are in poor physical condition, leading to high utility bills, unsafe and unhealthy indoor environments, and high GHG emissions.1 Those same families face greater exposure to harmful outdoor air pollution compared to others. The access and reliability of public transportation is disproportionately worse in neighborhoods with large populations of people of color, and large swaths of vulnerable neighborhoods, from East Boston to Mattapan, do not have ready access to the city’s bike network. Income inequality is a growing national issue and is particularly acute in Boston, which consistently ranks among the highest US cities in regards to income disparities. With the release of Imagine Boston 2030, Mayor Walsh committed to make Boston more equitable, affordable, connected, and resilient. The Summary Report outlined the broad strokes of how action to reach carbon neutrality intersects with equity. A just transition to carbon neutrality improves environmental quality for all Bostonians, prioritizes socially vulnerable populations, seeks to redress current and past injustice, and creates economic and social opportunities for all. This Carbon Free Boston: Social Equity Report provides a deeper equity context for Carbon Free Boston as a whole, and for each strategy area, by demonstrating how inequitable and unjust the playing field is for socially vulnerable Bostonians and why equity must be integrated into policy design and implementation. This report summarizes the current landscape of climate action work for each strategy area and evaluates how it currently impacts inequity. Finally, this report provides guidance to the City and partners on how to do better; it lays out the attributes of an equitable approach to carbon neutrality, framed around three guiding principles: 1) plan carefully to avoid unintended consequences, 2) be intentional in design through a clear equity lens, and 3) practice inclusivity from start to finish

MaaS surveillance : Privacy considerations in mobility as a service

Funding Information: König, D., Eckhardt, J., Aapaoja, A., Sochor, J.L., Karlsson, M., 2016. Deliverable 3: Business and operator models for MaaS. MAASiFiE project funded by CEDR. Submitted to: CEDR Conference of European Directors of Roads. Publisher Copyright: © 2019 Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Peer reviewedPostprin