Trafﬁc disruption modelling with mode shift in multi-modal networks Dong Zhao1 Adriana-Simona Mih ait a1 Yuming Ou1 Sajjad Shaﬁei2 Hanna Grzybowska3 Kai Qin2 Gary Tan4 Mo Li5and Hussein Dia2

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Trafﬁc disruption modelling with mode shift in multi-modal networks

Dong Zhao1, Adriana-Simona Mih˘

ait¸˘

a1, Yuming Ou1, Sajjad Shaﬁei2, Hanna Grzybowska3,

Kai Qin2, Gary Tan4, Mo Li5and Hussein Dia2

Abstract— A multi-modal transport system is acknowledged

to have robust failure tolerance and can effectively relieve urban

congestion issues. However, estimating the impact of disruptions

across multi-transport modes is a challenging problem due to

a dis-aggregated modelling approach applied to only individual

modes at a time. To ﬁll this gap, this paper proposes a new

integrated modelling framework for a multi-modal trafﬁc state

estimation and evaluation of the disruption impact across all

modes under various trafﬁc conditions. First, we propose an

iterative trip assignment model to elucidate the association

between travel demand and travel behaviour, including a

multi-modal origin-to-destination estimation for private and

public transport. Secondly, we provide a practical multi-modal

travel demand re-adjustment that takes the mode shift of the

affected travellers into consideration. The pros and cons of

the mode shift strategy are showcased via several scenario-

based transport simulating experiments. The results show that

a well-balanced mode shift with ﬂexible routing and early

announcements of detours so that travellers can plan ahead

can signiﬁcantly beneﬁt all travellers by a delay time reduction

of 46%, while a stable route assignment maintains a higher

average trafﬁc ﬂow and the inactive mode-route choice help

relief density under the trafﬁc disruptions.

Index Terms— multi-modal transport, trafﬁc states estima-

tion, disruption modelling, incident impact analysis, mode shift

I. INTRODUCTION

A. Background and motivation

Resilient cities have recently embraced a fully-connected

multi-modal transport network that gives travellers more

freedom when choosing when, where and how to travel.

However, multi-modal urban environments are also vulner-

able due to the lack of tolerance against an ever-growing

population, an expanding travel demand, a high private

car ownership, deﬁcient transport design, inadequate trafﬁc

control and ﬂawed travelling or driving behaviour [1].

To improve the efﬁciency of the transport system at a large

scale, the encouragement of a travel behaviour change and

active mode shift is an encouraging option studied recently

[2]. Many other research studies reinforce this initiative by

providing substantial evidence via data-driven, or simulation-

based approaches [3], [4], [5]. The data-driven approaches

capture the real trafﬁc behaviour before and after disrup-

tions, and some applications are used in programs such as:

INPHORMM, TAPESTRY or Travel Smart [6]. Other early

1University of Technology Sydney, Ultimo, NSW 2007, Australia. Cor-

responding authors contact: Dong.Zhao@student.uts.edu.au

2Swinburne University of Technology, Hawthorn, VIC 3122, Australia

3Data61, CSIRO, Eveleigh, NSW 2015, Australia

4National University of Singapore, Singapore

5Nanyang Technological University (NTU), Singapore

studies revealed the value of public transport by investigating

the change of trafﬁc states (e.g. section ﬂows, trafﬁc volumes

or travel times) and proposed an entire public transport

service removal when massive public transport disruptions

occur or when service is suspended [7], [8]. Few studies

that consider a simulation approach mention that the change

in the level of congestion before and after the removal

of public transport services would clarify the signiﬁcance

of public transport [9]. More recently, the unprecedented

COVID-19 pandemic has heavily modiﬁed the travel demand

and provided evidence with regards to the impact of trafﬁc

demand across all mode shifts in a city [10].

Challenges: All previous studies solve the mode choice

problem before departing, and most publications provide

modelling methods from a macroscopic or a mesoscopic

level based on a statical analysis. There is little research

into investigating the beneﬁts of an active mode shift from a

dynamic microscopic perspective and its impact when trafﬁc

disruptions occur. A signiﬁcant gap is present due to the lack

of data regarding the impacted demand under incidents and

active mode shifts. Some studies rely on surveys or a stated

preference obtained ahead of trips to obtain the number of

impacted travellers or the number of mode and route shift

[11], [12]. However, we emphasise identifying the impacted

origin-to-destination (OD) trips affected by disruptions in a

simulating model, and the change of mode and route choice

that leads to a demand change is employed for evaluating

the impact on network performance in our work.

Apart from the lack of data, quantifying the impact of

disruptions is also a major challenge; some research studies

have analysed the change of trip-based mean delay, mean

speed [13] or travel time [9]. However, such indicators can

hardly differentiate the impact from the general trafﬁc (e.g.

recurrent congestion) or from trafﬁc control strategies. To

address this issue, we work across several indicators versus

baseline conditions in order to evaluate the efﬁciency of the

proposed ones.

Another major challenge of dynamically simulating the

mode shift is the lack of dynamic demand data and the

method of integrating the OD estimation across different

transport modes in order to identify the impacted trips. Most

previous research studies only consider a single-mode [14],

while some research studies model car-based transportation

versus public transportation differently [15]. Extensive ev-

idence considers the OD estimation from a total genera-

tion and attraction data based on the gravity model. This

method has been largely developed with the improvement of

mathematical, analytical and computational skills. However,

arXiv:2210.06115v1 [physics.soc-ph] 12 Oct 2022

Fig. 1: Framework of our proposed multi-modal transport network modelling under disruptions.

the large potential of the gravity model approach in the

transport ﬁeld has not yet been fully explored, as most

research studies attempted to investigate the OD matrix for

a single transport mode, mostly cars. There is still a need

to consider the inﬂuence of other transport modes when

mode splitting and trip assigning under a multi-modal public

transport environment. The challenge of integrating the OD

matrices of various public transport modes with that of

private vehicles is still unsolved.

B. Paper Contributions

In this paper, we propose an integrated modelling approach

comprised of multiple stages, from the data selection and

ﬁltering to the origin-to-destination estimation modelling

across multiple modes, down to a dynamic assignment and

microscopic simulation modelling aimed at evaluating the

impact of disruptions across multiple modes. Finally, we

propose a mode shift impact modelling to evaluate the best

mitigation strategies and employ different disruption impact

indicators such as delay time, ﬂow, density and travel time

for identifying the impacts.

Another important contribution represents the investigation

of the mode shift behaviour according to dynamic trafﬁc

states; more speciﬁcally, we provide a method to examine

the change in trafﬁc states and the travel costs due to mode

and route shifts under trafﬁc disruptions. We model the

decision-making en-route and the mode choice relies on

an iterative trafﬁc assignment; this means that, for those

ﬂexible travellers, the route choice is modiﬁable during

their travelling, and the decision-making is more appealing

than those travellers who are loyal to the initial routing

plan. To summarise, the main theoretical and methodological

contributions of this paper are:

•an integrated OD estimation modelling framework for

multi-modal transport networks,

•a suitable spatial-temporal disruption impact modelling

via a multi-modal transport simulation approach,

•a dynamic trafﬁc assignment model that simulates the

mode shift behaviour via a dynamic demand adjustment,

•an analytic method regarding the mechanism of mode

shift as well as its impact under trafﬁc disruptions.

This paper is organised as follows. In Section II, the dy-

namic trip assignment model is discussed, and the details of

the integrated OD estimation is highlighted in Section II-B,

followed by the methodology of trafﬁc disruption modelling

and the procedures for determining the spatial-temporal

impact of trafﬁc disruptions in Section II-C. The model for

mode shift and impacted trips identiﬁcation are included in

Section II-D. The application of the proposed methods to a

real network is presented in Section III and the results of

the case study are demonstrated in Section IV. Finally, the

research conclusion and the future directions are provided in

Section V.

II. METHODOLOGY

A. Modelling framework

Fig. 1 showcases our proposed modelling framework

for evaluating the impact of disruptions across multi-mode

transport networks. The framework consists of three stages:

at Stage 0 we collect, ﬁlter and aggregate all the input

data-sets (such as trafﬁc ﬂow counts, trafﬁc control plans,

incident logs, etc.); at Stage 1 we propose a multi-modal

demand estimation modelling with the purpose of obtaining

an integrated multi-modal OD demand matrix (see details

in Section II-B); at Stage 2 we further propose a dynamic

trip assignment and demand reﬁnement based on the impact

of transport disruptions (as explained in Section II-C), and

ﬁnally, at Stage 3 we construct various mode and route shift

strategies and their impact on the trafﬁc congestion, as further

described in Section II-D.

B. Integrated multi-modal OD estimation (Stage 1)

The multi-modal transport system is ﬁrstly modelled by

implementing a four-step demand estimation model but

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Trafcdisruptionmodellingwithmodeshiftinmulti-modalnetworksDongZhao1,Adriana-SimonaMihait¸a1,YumingOu1,SajjadShaei2,HannaGrzybowska3,KaiQin2,GaryTan4,MoLi5andHusseinDia2AbstractAmulti-modaltransportsystemisacknowledgedtohaverobustfailuretoleranceandcaneffectivelyrelieveurbancongestionissues.Howe...

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Trafﬁc disruption modelling with mode shift in multi-modal networks Dong Zhao1 Adriana-Simona Mih ait a1 Yuming Ou1 Sajjad Shaﬁei2 Hanna Grzybowska3 Kai Qin2 Gary Tan4 Mo Li5and Hussein Dia2

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