Transportation Data Platform

Government:

City of San José

Category:

Digitization, Data Analytics, Data Collection, IoT, Mobility, AI/ML

Budget:

Budget Exists

Procurement Method:

RFP
View doc

Application Period:

December 11, 2019

Through

February 7, 2020 3:59 PM

Q&A Period:

December 11, 2019 - January 17, 2020

Challenge

The City of San José Department of Transportation is seeking a modern, comprehensive, and easy-to-use solution to aggregate, automated, and analyze historical and new datasets for our staff.

Background

An ongoing challenge for the Department of Transportation continues to be the integrating of datasets into a centralized system. Currently, the solution is highly manual, inefficient, and unreliable as data is locked within individual spreadsheets across divisions. In order to plan for the transportation system of the future, this issue needs to be resolved to provide a new, modern tool for data analysis. Additionally, as transportation data access is increasing at an unprecedented rate, DOT needs a scalable way to integrate and validate 3rd party datasets along with historical data. 

Requirements & Outcome

The Department of Transportation is seeking a solution that would have the following outcomes: 
  • AGGREGATE DATA – Utilize existing traffic count data from multiple sources and provide central access to all traffic count and crash/injury data.  Both real-time and historical data including:  Manual counts, induction loops, pneumatic tubes, cameras, volume, speeds, and crashes. 
  • ADD ADDITIONAL DATA SOURCES – Add conventional and unconventional data sources such as weather, infrastructure, events, mobile, vehicle-based, video and crowd-sourced data and derive insights on how each impact traffic. We are also interested to build a system that can overlay or link police crash data to public health trauma/injury data. 
  • DATA PREPARATION – provide automated methods to ingest, clean, and govern datasets across sources. 
  • AUTOMATE MANUAL PROCESSES & REPORTS – Calculate live and historical 24 hour traffic volume changes, Average Annual Daily Traffic (AADT), Peak Hour Factor (PHF), Vehicle Miles Traveled (VMT), as well as other important actionable insights 
  • ANALYZE THE DATA – Use machine learning (ML) and artificial intelligence (AI) to analyze all the data layers and develop an algorithm that can predict the behavior of traffic days in advance with a high level of certainty. 
  • DATA VALIDATION 
  • DATA PRIVACY – Privacy, transparency and public ownership of data.  Take proactive and comprehensive steps to ensure that the benefits to the public do not come at the expense of the privacy of individuals. 
  • DATA STORY – Provide resident-centric narratives and data to publish to and engage San Jose’s residents via the City Data Portal.  At minimum, include a high-level and visual summary article, a more detailed narrative analysis, and raw data used in those writeups.