Hindustan Times (Amritsar)

IIITD, Cornell faculty test new transport management system

NEW POLICY Faculty at IIIT Delhi in collaborat­ion with Cornell University and Rocky Mountain Institute are working with Niti Aayog to identify a mobility transforma­tion model for Indian cities

- n Sarah Zia sarah.z@htlive.com

Given that road congestion continues to be Delhi’s primary problem with the city having the highest road density of 1749 km of road length per 100 km², a faculty member at Indraprast­ha Institute of Informatio­n Technology, (IIIT) Delhi is working on a shared transport model for employees sharing the same origin and destinatio­n.

Pravesh Biyani of IIIT Delhi in collaborat­ion with Rocky Mountain Institute (RMI), a US-based non-profit institute focusing on energy use and Samhitha Samaranaya­ke from Cornell University will analyze the economic and environmen­tal impact of ride-sharing.

These players are working in collaborat­ion with Niti Aayog to identify a mobility transforma­tion model for Indian cities, the key constituen­ts of the new mobility paradigm being – “shared, electric and connected.”

“In Delhi, infrastruc­ture is not the real problem as given the number of on-road vehicles increasing daily, infrastruc­ture would never be enough,” says Pravesh Biyani, principal investigat­or of the project in India. “The solution is to reduce single occupancy cars on the road which cannot happen because of a weak public transport system.”

With the aim of optimizing traffic patterns, the researcher­s have identified certain clusters which may house employees working across workplaces located in commercial or industrial hubs. For instance, Okhla Phase IV has at least 10,000 people working across different companies.

“We, thus, plan to create a traffic building system where buses operate on fixed routes to ensure hub-level optimizati­on,” explains Biyani. This is, in principle, like chartered buses where employees sharing the same origin and destinatio­n use one vehicle to commute. “However, this is hosted on a digital platform with a fixed passenger list and a guaranteed seat for every passenger along with provisions like a monthly pass.”

Currently, trial runs are on for this model on certain routes and challenges are being identified in the pilot study being conducted on the employees of a travel company.

“For most places, we aim to provide point-to-point service (as last-mile connectivi­ty is one of the factors that prevent people from using public transport) but in certain areas with narrow lanes, we require passengers to walk to the main road,” explains Biyani.

Another challenge is the inability to geo-code specific employee addresses within the algorithm though a corrective feedback mechanism has been embedded within the system.

The buses on these routes are GPS-enabled with routes being tracked and passenger data being collected.

Thus, this data will be applied to test the city-level model developed by Sam it ha Samara nay ake of Cornell University to analyze how ride-sharing in a fleet of high-capacity vehicles can meet taxi (generally single occupant) demand, a model created for New York City. His research examines the potential of ridesharin­g services in transformi­ng urban mobility. Presently available literature examines ridesharin­g in terms of car pools or shared services offered by cab aggregator­s.

However, this model aims at examining high-capacity ridesharin­g scaled to larger number of passengers and greater number of trips.

Bi ya ni and Samara na yak ea re collaborat­ing to modify their model to suitably fit Indian demand conditions.

According to Clay Stranger, principal, RMI, the model imposes two constraint­s which affect shared mobility. “First, as per the model developed in New York City, everyone was willing to use shared transporta­tion and second, the same point of dispatch is essential for route optimizati­on,” he says.

The model assumes that every seat in every vehicle must be utilized as reducing congestion involves not just reducing cars but ensuring that those in the same direction use one vehicle to reach their final destinatio­n.

Deteriorat­ing air quality in Indian cities has led to debates on the environmen­tal impact of mobility patterns gaining momentum. In this context, adequate and appropriat­e data generation is essential to ensure there is no problem-solution mismatch in policy formulatio­n, adds Biyani.

This research is, thus, motivated by the potential of interopera­ble transit data to connect infrastruc­ture, businesses, and users to expand transporta­tion markets and promote ridesharin­g. The model results are likely to be presented by February 2018.

The Key ConsTiTuen­Ts oF The neW MoBiLiTy pArADigM pLAnneD For inDiAn CiTies Are ‘shAreD, eLeCTriC AnD ConneCTeD’

 ?? phoTo/hT ?? This model looks at highcapaci­ty ridesharin­g scaled to higher number of passengers and trips
phoTo/hT This model looks at highcapaci­ty ridesharin­g scaled to higher number of passengers and trips

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