Utilizing Data Analytics for Optimum Urban Transportation System

This study addresses the challenges associated with urban transportation by providing a framework for exploiting data analytics, with application to transportation data, to achieve an effective and time-efficient metropolitan city transportation system. We aim to understand traffic in different areas of the city, as well as trying to categorize the various zones within numerous areas in the city, such as: business destination, residential destination, or touristic destination according to its popularity given both the time-range and the day of the week. In this project, a logistic regression classification model is built to classify locations into hotspots/non-hotspots

Visual Analysis of Place Connectedness by Public Transport

The concept of place connectedness (traditionally termed `accessibility') refers to the ability of people to reach various services and to participate in activities. Connectedness by public transport is especially important for underprivileged and elderly people while the active use of public transport by the general population contributes in reducing traffic congestions and air pollution in cities. Place connectedness analyses are performed for a variety of purposes. In communication with transportation experts, we performed the conceptual modeling of the domain of problems related to place connectedness, defined the system of analysis tasks, and matched the tasks to visual analytics techniques that are capable to support them. In this paper, we introduce the task typology and present the visual analytics techniques using several example scenarios of place connectedness analyses

Scalable system for smart urban transport management

Efficient management of smart transport systems requires the integration of various sensing technologies, as well as fast processing of a high volume of heterogeneous data, in order to perform smart analytics of urban networks in real time. However, dynamic response that relies on intelligent demand-side transport management is particularly challenging due to the increasing flow of transmitted sensor data. In this work, a novel smart service-driven, adaptable middleware architecture is proposed to acquire, store, manipulate, and integrate information from heterogeneous data sources in order to deliver smart analytics aimed at supporting strategic decision-making. The architecture offers adaptive and scalable data integration services for acquiring and processing dynamic data, delivering fast response time, and offering data mining and machine learning models for real-time prediction, combined with advanced visualisation techniques. The proposed solution has been implemented and validated, demonstrating its ability to provide real-time performance on the existing, operational, and large-scale bus network of a European capital city

What California Gains from Reducing Car Dependence

Cars provide an unparalleled level of mobility but have negative financial, public health, environmental, and social impacts. Reducing the need for driving in California would produce a range of household- and community-level benefits. Driving is associated with adverse health effects (e.g., obesity, high blood pressure, depression, injuries, fatalities), while commuting by walking or biking provides numerous physical and mental health benefits. A reduction in driving would also improve public health by decreasing air pollution and greenhouse gas emissions. It would save substantial sums of money:  households spend about $9,000/year or 16$500 million per year on highway maintenance. A less car-dependent society would also be more equitable for those with limited income or limited physical abilities who cannot drive, to the benefit not just of those individuals but the community as a whole. While it is not realistic in the foreseeable future for most Californians to live without their cars, it is possible to decrease car dependence. Doing so requires a shift away from a century-old prioritization of the goal of reducing vehicle delays over other important goals. Creating a less car-dependent world is not necessarily more costly to the public and can be achieved over time through changes in land use and transportation planning practices. Answers to many of the frequently asked questions about such efforts are provided.View the NCST Project Webpag