Workdiamond2024

Travel Demand Estimation for a Special Event using Pervasive Data: A Case Study of G20 Summit

Piyush Lalwani · Ashutosh Kaushal · Sai Chand · Steven Travis Waller

5 citations0/0 files storedProcedia Computer Science

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Transportation Planning and Optimization Traffic Prediction and Management Techniques Human Mobility and Location-Based Analysis

Abstract

Due to globalization and rapid development, the demand for travel and congestion are increasing, stressing the road networks. Furthermore, road networks are subjected to planned and unplanned disruptions, and these disruptions affect the road network performance by either damaging its components, fluctuating the travel demand, or doing both at the same time. Thus, assessing the change in travel demand and travel patterns during such disruptions is essential. Conventionally, travel demand has been estimated using a household travel survey (HTS). However, the HTS is not feasible for disrupted conditions because of its large sample size requirement and time-consuming methodology. However, with the advent of crowdsourced data, new methodologies have been proposed for estimating the travel demand, but most of these studies have focused on estimating demand for a typical day and not disrupted scenarios. Moreover, data sources utilized till now have low sample sizes or are unavailable for developing countries. To bridge this gap, the current study utilizes an automated planning tool called Rapidex to estimate the travel demand and pattern change for a planned disruption, i.e., the 18th G20 Summit, Delhi, using crowdsourced travel time data from TomTom API. Upon comparing the results of travel demand and travel patterns of typical days with G20 days, it was observed that travel demand was lower during G20. Moreover, it was observed that the zones where railway stations, interstate bus terminals, and G20 activities were focused had higher generation and attraction share than typical days. Other zones within the regulated area had lower generation and attraction rates, which can be because of travel restrictions imposed in these zones.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 5 citations exist upstream.

References

2015Origin–destination trips by purpose and time of day inferred from mobile phone data 2011Estimating Origin-Destination Flows Using Mobile Phone Location Data 2015Time-evolving O-D matrix estimation using high-speed GPS data streams 2014Origin-Destination Estimation for Non-Commuting Trips Using Location-Based Social Networking Data 2017Origin-Destination Trip Matrix Development: Conventional Methods versus Mobile Phone Data 2005Population Origin–Destination Estimation Using Automatic Vehicle Identification and Volume Data 2021Rapidex: A Novel Tool to Estimate Origin–Destination Trips Using Pervasive Traffic Data 2021Single-level approach to estimate origin-destination matrix: exploiting turning proportions and partial OD flows

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.