Page | 1 The Influence of Low Traffic Neighbourhood Scheme on Multimodal Traffic Flow in London Xianghui Zhang*1, Tao Cheng†1 1 SpaceTimeLab for Big Data Analytics, Department of Civil, Environmental and Geomatic Engineering, University College London, London WC1E 6BT, UK GISRUK 2023 Summary This study aims to investigate the influence of Low Traffic Neighbourhood (LTN) Scheme deployed since the COVID-19 pandemic on multimodal traffic flow in London. We adopt a mobile phone application dataset to investigate the changes in multimodal traffic flow generated by the general public following the introduction of LTNs. Three LTNs located in London are explored between 4th May and 30th August 2020. The analysis approved that LTN scheme could encourage residents to take cycling and restrict through-traffic, but the influence varies across areas, travel modes and groups and may affect by specific measures. KEYWORDS: Multimode, Traffic flow, Low Traffic Neighbourhood, London 1. Introduction After the outbreak of the COVID-19 pandemic in early 2020, the LTN schemes have been quickly delivered in several London boroughs to make it easier for active travel to keep social distance. These new LTNs are implemented based on the combination of local knowledge and TfL’s Strategic Neighbourhoods Analysis (Transport for London, 2020). Based on this analysis and decision-making, several combinations of intervention measures are adopted to make up LTNs, such as modal filters, bus gates, traffic signals. After deploying these measures, the through-traffic will be restricted to access the intervened areas, but residents’ vehicles and public services vehicles can still arrive at the destinations within the LTNs. Existing research explored the influences of LTN schemes deployed before and after the pandemic on impacts on active travel (O’Malley, 2021), road safety (Goodman et al., 2021), and health (Laverty et al., 2021). These existing studies contributed useful knowledge for evaluating LTN schemes, but their direct influences on preventing driving through traffic and mode shifting are rarely examined due to the limitations of available data sources. Although some local authorities monitored the changes in traffic flow after introducing LTNs through traffic count sites, this data only reported the driving flow on limited road segments. Besides, existing research only focused on the responses of residents (via social survey) but ignore the well-being of people with trip attractions (i.e., workers and visitors) and pass-through people, which are also important components of local mobility. Besides, existing research did not reveal the responses of cycling and walking flow to LTN scheme, which is one of the important targets of LTN scheme. To address the gap in existing research, this paper aims to evaluate the impacts of LTNs on mobility by analysing the multimodal traffic flow in the general public, including residents, people with trip attractions, and pass-through people. We will generate a multimodal traffic flow dataset based on an individual multimodal derived from Mobile Apps GPS data. Three LTNs deployed in London from * xianghui.zhang.20@ucl.ac.uk † tao.cheng@ucl.ac.uk