Improving Traffic Safety by Leveraging Data and Emerging Technologies

Day

Lecture/

Tutorial

Lecture/Tutorial Name

Day 1

Lecture 1

Overview and introduction to local/regional/national roadway safety strategies and road safety risk assessment; the Safe Systems Approach and illustrative international examples.

Day 1

Lecture 2

Overview of traffic safety in India; geographic and roadway type contexts; historical safety trends; emerging concerns; data availability and accessibility considerations.

Day 1

Tutorial 1

Instructors facilitate the session to have participants work in small teams to highlight related needs, opportunities, and challenges in the Indian context.

Day 2

Lecture 3

Identify core aspects of road safety analyses including engineering, social-behavioral, law enforcement, emergency medicine, and educational components, data and technology considerations and needs to quantify safety.

a)     Spatial and temporal characteristics: e.g., rural or urban; high-speed or low-speed roads; day or night

b)    Excessive speeds, driving under the influence, distracted driving, wrong-way driving

c)     Vulnerable road users: pedestrians, bicyclists

d)    Small motorized vehicles: 2-wheelers and cars

e)     Commercial vehicles: buses, trucks/lorries

f)     Licensing and registration

g)    Repeat offenders/violators

Day 2

Lecture 4

Leverage traditional data systems and technologies; these include datasets related to

a)     Crash (accident) reports

b)    Driver’s license records

c)     Citation (challan) data sets

d)    Paper-based vis-à-vis electronic recording systems

e)     Aggregation of data from local to centralized systems

Day 2

Lecture 5

Crash process: factors of road crashes a. driver or human factors b. roadway factors c. vehicle factors

Day 2

Tutorial 2

This session is intended to provide participants with opportunities to work with various real and hypothetical data sets: evaluate individual data sets; and identify technology opportunities, gaps, or shortcomings in the data.

Day 3

Lecture 6

Leverage emerging data systems and technologies; examples include

a)     Datasets related to driver’s license records, vehicle registration records, challans, etc.

b)    Mobile phone technologies

c)     In-vehicle technologies to derive location, speed, acceleration, etc.

d)    Road infrastructure-based sensors and sensor systems

e)     Model Minimum Uniform Crash Criteria (MMUCC) adopted in the USA

f)     Integrate/link the aforementioned datasets

Day 3

Lecture 7

Leverage traditional and emerging data systems in India; these include

a)     The Integrated Road Accidents Project (IRAD) implemented by India’s MoRTH

b)    Emerging opportunities, technologies, and data sources

Day 3

Tutorial 3

In this session, participants will build on the previous tutorial to work with data sets, integrate / link data; analyze the integrated database; identify gaps or shortcomings in the data, and opportunities to close or fill the gap including technology-based options.

Day 4

Lecture 8

Metrics to evaluate safety risks and strategies to integrate data from varied and disparate sources

a)     Develop metrics to evaluate safety risks

b)    Develop modeling approaches to evaluate safety risks and quantify risk/safety indicators based on key criteria, critical contributing, and causal factors

c)     Identify common fields and links across two or more of the data sets

d)    Integrate/link the aforementioned datasets

Day 4

Lecture 9

Black spot identification and remedial measures: the Indian Roads Congress (IRC) approach.

Day 4

Tutorial 4

This session is to have participants wrap up work initiated in the previous tutorials to work with data sets: integrate / link data, analyze the integrated database; identify technology opportunities, gaps, or shortcomings in the data; and synthesize the results.

Day 4

Lecture 10

Discuss alternative approaches to evaluate safety risks

Day 5

Lecture 11

Synthesize the elements and approaches identified to develop/propose frameworks and approaches for data-aided, technology-enabled support decision-making.

a)     Policy-making: legislative, regulatory

b)    Design

c)     Operations

d)    Law enforcement (police actions and strategies)

Day 5

Tutorial 5

Participants will work in teams to apply the data sets to alternative frameworks and approaches to support synthesized data-aided, technology-enabled decision-making.