Future of ITS: Five Trends to Watch

and Why

Santosh Mishra

Senior Transportation Planner

March 19, 2013

2

Presentation Overview

Needs

Trends to watch

Relevance to Transit

Gaps/Issues

Potential Adoption Strategies

Source: USDOT

3

Needs

Smarter planning

System/customer

feedback

Improved intermodal

operations

Smarter decision tools

Data acquisition,

management and

processing

Vehicle safety and

security

Customer safety and

security

Seamless customer

mobility

Ubiquitous customer

information

Fast and universal

payment tools

4

Applicable Future Trends

Cloud computing

Info-mobility

Crowdsourcing

Big data

Connected vehicle

technologies

Source: USDOT

5

Cloud Computing: Relevance to Transit

Improved system performance (e.g., high performance

servers)

Reduced cost

Capital investment

Recurring infrastructure cost

Required labor or other resources

Ubiquitous access (e.g., tablets, smartphones)

Easier update/upgrade process

No constraints on storage

Flexible licensing models (e.g., subscription-based)

6

Cloud Computing: Gaps/Issues

Single point of failure

Level of vendor support

Information security

Limited control

Privacy concerns

Integration with third party

systems

7

Cloud Computing: Potential Adoption Strategies

Build culture for cloud: balance content creation &

consumption

Enhance ongoing adoption strategies

Back-office software (e.g., Google apps, ERP/CRM tools)

Operations software (e.g., scheduling software, CAD/AVL, RTIS)

Other analysis software (e.g., ESRI products)

Focus on high availability

Enhance security

Focus on 24x7 support

8

Info-Mobility: Relevance to Transit

Mobile revolution

Connectivity

Convergence

Communication

Advanced location-based technologies

Indoor mapping

Augmented reality

Advanced communication technologies (e.g., NFC,

WiMAX/LTE, Ultra wide-band, SuperWiFi)

Machine-2-machine (M2M) communication

9

Info-Mobility: Gaps/Issues

Mobile ecosystem fragmentation

High customer expectations

Network latency

Big data processing

Cross-device consistency

Lack of institutional agreements

Title VI compliance

Unclear return on investment

10

Info-Mobility: Potential Adoption Strategies

Build mobile strategy

What

When

How

Enhance current mobile offerings

Enhanced real-time information

dissemination

Focus on mobile payments

Training/education

Build partnerships

Enhance data security

11

Crowdsourcing: Relevance to Transit

Enhanced public participation

Reduced data collection cost

Travel behavior research

Enhanced talent access

Enhanced data access

12

Crowdsourcing: Gaps/Issues

Technical

Limited data processing capability

Limited storage

Data quality

Noisy dataset

Bias/non-random set

Data interpretation and

correlation

Consensus building

Justification

13

Crowdsourcing: Potential Adoption Strategies

Piggyback on mobile strategy

Educate customers/partners

Build crowdsourcing products

Use or to fetch customer preference on trip

alternatives and offerings

Obtain customer feedback on infrastructure (e.g.,

www.fixmytransport.com), safety/security, real-time delay status

Use apps for public participation (e.g., UTA, Salt Lake City)

Source and mine public data (e.g., Twitter, Facebook)

Partner with existing app providers (e.g., Moovit, Waze,

Tiramisu)

14

Big Data: Relevance to Transit

Planning

Smarter networks

Better integration with other modes

Better products for riders

Operations

Regional/intermodal operations centers

Anomaly prediction & scenario modeling

Impact mitigation & service restoration

Real-time traveler information

Multimodal info acquisition and dissemination

Managing crowdsourced information

Source: KatInsight.com

15

Big Data: Relevance to Transit (continued)

Safety/security & customer service

Real-time video analytics

Multimedia playback (e.g., audio, video, social media,

traffic, weather) using archived data

Fare payments

Universal mobile payment (e.g., transit, parking, toll, retail)

Link with other data sources to create incentives (e.g.,

Google Offers, Groupon, brand loyalty programs)

Intermodal demand modeling (e.g., O-D modeling and

transfer analysis)

16

Big Data: Gaps/Issues

Technical:

Paradigm shift:

Data analysts to data scientists

SQL to NoSQL

Big data platforms still not

mainstream

Storage & server

performance

Institutional:

Data privacy

Lack of information sharing

agreements

Financial:

Cost of current big data

platforms

Immediate benefits from

conventional data mining

17

Big Data: Potential Adoption Strategies

Standardize data exchange protocols

Focus on incremental progress

Enhanced planning (e.g., use of crowdsourced data)

Enhanced customer information (e.g., utilization of weather, traffic

and social media datasets)

Build partnerships

Citywide/Statewide initiatives

Private enterprises

Independent developers

Source: SAP

18

Connected Vehicle Technologies: Relevance to Transit

Safety

Vehicle to infrastructure (V2I)

Vehicle to vehicle (V2V) (e.g.,

crash/collision avoidance)

Mobility

Real-time data capture

Dynamic mobility applications (e.g.,

IDTO)

Environment

AERIS (e.g., TSP, alternative fuel)

Road weather applications

Source: USDOT

19

Connected Vehicle Technologies: Gaps/Issues

Interoperability issues

Information silos

Institutional issues

Communications

infrastructure

Data acquisition and

processing challenges

Stakeholder involvementSource: USDOT

20

Connected Vehicle Technologies: Potential Adoption

Strategies

Strategic planning

Align with technology evolution trend

Focus on “low hanging fruit”-stay away from bleeding edge

Build regional ecosystem

Multimodal coordination infrastructure (e.g., TMCC model)

Big data acquisition, management and use platforms

Institutional agreements

Pilot programs (e.g., Ann Arbor safety pilot, IDTO)

Thank You

Contact:

Santosh Mishra

38 Chauncy St, Suite 200

Boston, MA 02111

Tel: 857-453-5466

Email: skmishra@transystems.com