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Trends and Future Challenges in Congestion Management

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Traffic congestion creates multidimensional impacts that require stakeholders' integration and coordination. This paper tries to close the research gaps in congestion management by examining a case study of integrated solutions of congestion measures and analyzing future challenges in congestion management based on two selected factors. The authors develop the result from the literature study and an expert interview that provides a better perspective on the case study. The study generates a new perspective on reviewing the organizational aspect of integrated congestion management measures. Secondly, it starts a discussion on future challenges in congestion management and connects the domain of future mobility with congestion theories as an independent discussion.
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Irfansyah et al.
Current Trends
and Future Challenges
in Congestion Management
WiP Paper Practitioner-centered Logistics and Supply Chain Management in Crisis Response
Proceedings of the 17th ISCRAM Conference Blacksburg, VA, USA May 2020
Amanda Lee Hughes, Fiona McNeill and Christopher Zobel, eds.
Current Trends and Future Challenges
in Congestion Management
Aditya Irfansyah
University of Münster
irfansyah@uni-muenster.de
Adam Widera
Chair of Information Systems and Supply
Chain Management
University of Münster
adam.widera@ercis.uni-muenster.de
Mark Haselkorn
Human Centered Design & Engineering
University of Washington
markh@uw.edu
Bernd Hellingrath
Chair of Information Systems and Supply
Chain Management
University of Münster
bernd.hellingrath@ercis.uni-muenster.de
ABSTRACT
Traffic congestion creates multidimensional impacts that require stakeholders’ integration and coordination. This
paper tries to close the research gaps in congestion management by examining a case study of integrated solutions
of congestion measures and analyzing future challenges in congestion management based on two selected factors.
The authors develop the result from the literature study and an expert interview that provides a better perspective
on the case study. The study generates a new perspective on reviewing the organizational aspect of integrated
congestion management measures. Secondly, it starts a discussion on future challenges in congestion management
and connects the domain of future mobility with congestion theories as an independent discussion.
Keywords
Congestion Management, Traffic Incident Management, Intelligent Transportation System, Traffic Management
System, Future Mobility, Social Evolution, Future Challenges.
INTRODUCTION
Transportation plays a significant role in shaping human society's advancements (McDaniel 1972). It is one of the
main infrastructures that provide vital services to modern society. Physical components of transportation have
long lifetimes and require a longer time to change. In fast-growing or densely populated areas, it could lead to
many negative impacts; one of them is traffic congestion (Loorbach et al. 2010).
Agglomeration and rapid increase in the urban population creates a higher need for transportation infrastructure
and consequently will create traffic congestion problem if they are not correctly planned. Traffic congestion itself
is a serious problem that creates multidimensional impacts, including social, economic, and environmental (Li
2005). Therefore, it also needs the integration and coordination of stakeholders in the respective dimensions to
solve congestion. Many large urban areas will never solve all of their congestion problems but rather need to
manage it to create a reliable and predictable travel condition (ECMT 2007). In a crisis situation, congestion is a
major bottleneck that could limit the successful implementation of crisis response. On the other hand, the ability
to manage congestion in the normal situation might represent a higher degree of readiness and resilience towards
congestion in the time of crisis.
A large number of existing studies that tried to propose or review information systems (IS) addressing congestion
management (CM) measures can be found under several technological terms. Examples of those terms are Traffic
Management Systems (TMS) (Al-Sakran 2015, Djahel et al. 2015 and Nellore and Hancke 2016) or Intelligent
Traffic System (ITS) (Hernández et al. 2002, Chattaraj et al. 2009, Khekare and Sakhare 2013 and Roy et al.
622
Irfansyah et al.
Current Trends
and Future Challenges
in Congestion Management
WiP Paper Practitioner-centered Logistics and Supply Chain Management in Crisis Response
Proceedings of the 17th ISCRAM Conference Blacksburg, VA, USA May 2020
Amanda Lee Hughes, Fiona McNeill and Christopher Zobel, eds.
2017). However, the majority of studies discussed the technological aspects of the measures rather than the
organizational and social environment that is being served.
The topics of future mobility are considered as well-studied topics. As an example, Scopus indexed more than ten
thousand documents that are related to “autonomous vehicles,” one of the primary goals of future mobility. In a
broader social context, the long lifetimes and inertia of transport infrastructure had led researchers or agencies to
create long-term future scenarios and planning. These are ranging from a modest horizon of five to twenty years
(Ubbels et al. 2000, EC 2004 and Eurofound 2008) to a very long horizon of forty to fifty years (FTAG 2001,
Krail et al. 2007, WEC 2007 and Petersen et al. 2009).
A closer look at the literature on CM reveals research gaps that can be divided into two main areas. The first area
is reviewing CM measures that use new technology in their organizational aspects. Even though getting the
technology itself ready for implementation is the main goal, discussing the organizational environment where the
technology will be implemented is also important to have a successful implementation and full benefit. Another
area that lack of research is a separate analysis of the future in traffic congestion. Currently, this topic is discussed
as part of the studies that analyzed the future of mobility in general or how the novel technology is better in dealing
with current congestion problems. This research tries to close the gap by collecting the discussions from literature
and connecting them to the fundamental theories of congestion.
To sum up, the objective of the paper is to explore the definition and aspects of congestion, current trends with an
example of integrated solutions in managing congestion, and future challenges in CM caused by selected factors.
We use literature studies as the main source of information. We reflect those findings by including documents
from an ongoing project of Traffic Incident Management and Congestion Management (TIM-CM) by Seattle Area
Joint Operations Group (SAJOG) in Seattle, Washington, USA, as a case study of proposed integrated solutions
that followed the current trend of technology. This project set as an example of a possible means of multi-
organizational collaboration in managing congestion.
The paper is organized into four sections. The following section sets a background by discussing the definition
and aspects of congestion in transportation. In the next section, we explore the solutions in preventing or managing
congestion by listing and grouping discussed measures from several studies. This is followed by a case study from
an ongoing project in integrated CM as an example of the current trend. In the third section, we describe and
discuss the future challenges of congestion caused by selected factors. Finally, the conclusion is presented in the
last section.
RELATED WORK
Congestion in Transportation
Definition
Congestion in transportation is a complicated state. It is a situation where a physical phenomenon of vehicles
impedes each other’s’ progression that is described subjectively related to each user's expectations (ECMT 2007).
Therefore, there is no universal definition that can describe congestion objectively.
Goodwin and Dargay in ECMT (1999) defined congestion as "the impedance of vehicles impose on each other,
due to the speed-flow relationship, in conditions where the use of a transport system approaches its capacity."
Cambridge Systematics and Texas Transportation Institute (TTI) (2005) defined congestion as an excess of
demand on a portion of a roadway at a particular time that is shown by stopped or stop-and-go traffic conditions.
Similarly, VCEC (2006) defined congestion as a "situation where the demand for the use of roads is excessive,
resulting in slower than normal speeds." Li (2005) put a reflection of these perspectives on congestion in figure 1
below, where two critical points of congestion are illustrated. The first one is the high concentration of demand,
whereas the second one is the state where the capacity of the road is almost fully used. The figure shows that the
traffic speed will decrease exponentially as the traffic flow gets closer to the maximum road capacity. There is no
standard on determining the starting point of traffic congestion as it depends on subjective assessment. Therefore,
the below figure only presents an estimated position of the traffic congestion.
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Irfansyah et al.
Current Trends
and Future Challenges
in Congestion Management
WiP Paper Practitioner-centered Logistics and Supply Chain Management in Crisis Response
Proceedings of the 17th ISCRAM Conference Blacksburg, VA, USA May 2020
Amanda Lee Hughes, Fiona McNeill and Christopher Zobel, eds.
Figure 1. Speed-flow relationship and traffic congestion (Li 2005)
Framework
The discussion on congestion usually starts with how congestion happened and how to manage it. As shown in
figure 2 below, Li (2005) described the causal effect between what created congestion and the measures that
transport management used to manage it.
A more specific discussion on congestion causes and its categorization will be discussed in the subsection below.
Meanwhile, CM measures as responses to traffic congestion will be discussed in the later chapter. The causal loop
in this framework suggests that the failure in responding to congestion might fuel the causes and created more
severe congestion.
Figure 2. The Framework of Congestion (Adapted from Li (2005))
Causes
Congestion is usually classified into one of the two categories depending on its cause, whether it is recurrent or
non-current congestion (ECMT 2007). As the name suggests, recurrent congestion is congestion that happens
regularly or periodically on a section of the transportation system. On the other hand, non-recurrent congestion is
congestion that happens randomly and unexpectedly.
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Irfansyah et al.
Current Trends
and Future Challenges
in Congestion Management
WiP Paper Practitioner-centered Logistics and Supply Chain Management in Crisis Response
Proceedings of the 17th ISCRAM Conference Blacksburg, VA, USA May 2020
Amanda Lee Hughes, Fiona McNeill and Christopher Zobel, eds.
There are two different views on which category attributed to an instance of congestion. Skabardonis et al. (2003)
and Li (2005) use incidents, or the absence of it, as the deciding factor. Their view, as depicted in figure 2 above,
is more conservative compared to ECMT (2007). ECMT (2007) included planned events, such as road works,
sporting events, and any large events that attracted large masses into non-recurrent congestion. Similar to that
view, Systematics and TTI (2005) put out physical bottlenecks from irregular causes in their list of seven root
causes of congestion. Six causes that are listed as irregular congestions are traffic incidents, work zones, weather,
traffic control devices, special events, and fluctuations in normal traffic. Following the latter perspective, Talukdar
et al. (2013) did a literature review on this topic and created a diagram on the causes and their specific example,
as shown in figure 3 below.
Figure 3. Causes of Traffic Congestion (Talukdar et al. 2013)
Factors
As opposed to congestion causes described in the previous section, congestion factors are deciding on how fast
and severe congestion will occur. There are three categories of factors, namely micro-level factors that relate to
how traffic is on the roadway, macro-level factors that relate to usage demand, and exogenous factors that relate
to activity patterns (ECMT 2007). All of these factors distinguished by how they affect congestion. Factors that
are labeled as congestion triggers are the factors that will immediately raise traffic congestion at the micro-level.
Meanwhile, factors that are labeled as congestion drivers are the factors that contribute to the creation of
congestion incidence and the severity of it. These three factors and the causality are depicted in figure 4 below.
Causes
Recurrent
(from transport
system)
Non-Recurrent
(from traffic
influencing
events)
- Population and economic growth
- Desire to travel by private vehicles
- Unawareness of full cost of driving
- Influence of land use pattern
- Concentration of work trips in time
- Day to day variability in demand
Shortage of
infrastructure supply
Physical bottleneck /
insufficient capacity
Improper traffic control
and management
- Lack of investment in infrastructure
- Poor traffic control devices
- Ineffective management system
Excess demand
Traffic incidents
Special events
Work zones
Emergency situation
Poor weather
- Vehicular crashes
- Breakdowns
- Debris in travel lanes
- Events occur on the shoulder/roadsides
- Incidents off of the road way
- Construction activities
- Reduced visibility
- Bright sunlight on the horizon
- Presence of fog or smoke
- Wet, snowy or icy roadway
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Irfansyah et al.
Current Trends
and Future Challenges
in Congestion Management
WiP Paper Practitioner-centered Logistics and Supply Chain Management in Crisis Response
Proceedings of the 17th ISCRAM Conference Blacksburg, VA, USA May 2020
Amanda Lee Hughes, Fiona McNeill and Christopher Zobel, eds.
Figure 4. Factors of Congestion (Adapted by ECMT (2007) from Bovy and Hoogendoord (2000))
Congestion Management
Discussed Measures
As discussed in the previous chapter, congestion is a problem that emerges globally. It has led many works of
literature from different parts of the world, such as ECMT (2007), Li (2005), Strickland and Berman (1995),
Talukdar et al. (2013), and Downs (2005), to discuss management measures that can be implemented to relieve
congestion. Management measures of congestion are usually divided into two basic categories, namely demand-
side CM measures and supply-side CM measures. Although all the literature reviewed in this section agree with
the distinction, some measures, such as pre-trip guidance/information, might fall into the different side in different
literature.
Supply-side CM measures concerns with the availability of transport facilities itself and its capacities. With traffic
engineering techniques, these measures aim to improve the traffic flow for all users (Li 2005). Prepared from five
literature cited in this section, Table 1 below lists measures that fell into supply-side CM measures.
On the other hand, demand-side CM measures are designed to address and reduce traveler demand on
transportation systems (Strickland and Berman 1995). These measures can be implemented as a regular or on a
specific time to manage non-recurrent congestion or time-specific recurrent congestion. Table 2 below lists
measures that fall into demand-side CM measures prepared from five literature cited in this section.
626
Irfansyah et al.
Current Trends
and Future Challenges
in Congestion Management
WiP Paper Practitioner-centered Logistics and Supply Chain Management in Crisis Response
Proceedings of the 17th ISCRAM Conference Blacksburg, VA, USA May 2020
Amanda Lee Hughes, Fiona McNeill and Christopher Zobel, eds.
Table 1. Supply Side CM Measures
Group Measures
Expanding transport
infrastructure
Modifying existing infrastructure
Expanding the road system
Building new infrastructure
Rail construction
Improving public
transport
Developing mass transit
Bus lanes/high occupancy vehicle lanes
Better public transport services
Extending services
Adopting fee structures
Operational improvement
Public transport information provision
Improving traffic
operation
Traffic signal improvement
Traveler information systems
Incident management plans
Road traffic information systems
Pre-trip guidance / information
Monitoring and management of traffic flows
Managing freight operation
Mobility management Ridesharing
Promoting bicycle and pedestrian travel
Park and ride facilities
Appropriate institutional
arrangement
Multi-level framework for planning and decision making
Better coordination between national, regional and local
The right combination of
policies
A well-developed congestion management strategy
Developing congestion indicators
Appropriate monitoring plan
Ensuring peoples participation at the policy-making process
Table 2. Demand Side CM Measures
Group Measures
Economic measures Taxation/disincentives (e.g., Congestion / Cordon charges, Electronic
Road Pricing (ERP), Linked based pricing system and Road tolls)
Subsidies/incentives (e.g., incentives to the user of ridesharing)
Mixed-use toll roads
Area licensing scheme
Public transportation pass program
Regulatory measures Access management or restricted zones
Parking control
Traffic calming
Flexible working hours
Trip ordinances
Land-use policies Land-use planning
Site amenities and design
Communications
substitutes
Teleconferencing
Teleshopping
Remote working
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Irfansyah et al.
Current Trends
and Future Challenges
in Congestion Management
WiP Paper Practitioner-centered Logistics and Supply Chain Management in Crisis Response
Proceedings of the 17th ISCRAM Conference Blacksburg, VA, USA May 2020
Amanda Lee Hughes, Fiona McNeill and Christopher Zobel, eds.
Case Study
The case study on this paper discusses the TIM-CM project that is being conducted in Seattle, Washington, USA,
by a regional consortium called SAJOG (SAJOG 2017). This project aims to manage congestion in the Seattle I-
5 corridor by enhancing mobility and reducing congestion impact from traffic incidents (CoSSaR 2017). The
discussion in this section is based on two documents related to the project and an interview with Mark Haselkorn,
the project manager of TIM-CM from the University of Washington (UW). The interview helps to clear and
emphasize the critical contents of the documents.
The discussion is opened by two subsections that discussed the background of the projects, the managed workflow,
the challenges that were discovered, and the solutions that are proposed to solve the challenges. Then, this research
explores the organization of several aspects in this project, which are roles of multiple agencies and entities that
are managed in the scope of the projects, layering of systems and services that house technologies that are
implemented in the projects to help manage congestion; and implementation process.
From the list of CM measures in the previous section, this project can be attributed to some of the supply-side
measures that are listed in table 3 below.
Table 3. CM Measures Addressed by TIM-CM
Group Measures
Improving traffic operation Traveler information systems
Incident management plans
Road traffic information systems
Pre-trip guidance / information
Monitoring and management of traffic flows
Appropriate institutional
arrangement
Multi-level framework for planning and decision making
Better coordination between national, regional and local
The right combination of
policies
A well-developed congestion management strategy
Developing a congestion indicator
Appropriate monitoring plan
1. Integrated Workflow
In the initial phase of the project, two teams of Traffic Incident Management (TIM) and CM modeled and refined
their processes to create an integrated TIM-CM workflow, as shown in figure 5 below. TIM joint operation
consists of Washington State Patrol (WSP), Seattle Police Department (SPD), Incident Response Team from
Washington State Department of Transportation (WSDOT), and WSP 9-1-1 dispatch. Meanwhile, CM Joint
Operation consists of WSDOT Traffic Management Center (TMC), Seattle Department of Transportation (SDOT)
Transportation Operations Center (TOC), SPD traffic division, and SPD 9-1-1 dispatch.
The integrated workflow is modeled in a time-based diagram starting from the moment that an incident was
occurred (T0) to the end state where the normal traffic situation can be observed (T10). In managing incidents, TIM
focuses on life-saving and incident clearance. Meanwhile, CM focuses on the congestion caused by the incident
itself. Although TIM and CM have different focuses on the impacts of an incident, both need incident information
to enable successful collaborative management in mitigating the situation (CoSSaR 2017).
This integrated TIM-CM is a core component of Integrated Corridor Management (ICM) that is implemented on
the proposed Virtual Command Center (VCC). VCC will support the enhanced inter-agency coordination of ICM
in managing traffic incidents (SAJOG 2017).
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Irfansyah et al.
Current Trends
and Future Challenges
in Congestion Management
WiP Paper Practitioner-centered Logistics and Supply Chain Management in Crisis Response
Proceedings of the 17th ISCRAM Conference Blacksburg, VA, USA May 2020
Amanda Lee Hughes, Fiona McNeill and Christopher Zobel, eds.
Figure 5. As-Is TIM-CM (CoSSaR 2017)
2. Challenges and Solutions
The project group found the challenges to construct a coordinated TIM-CM in both conceptual phases of analyzing
the TIM-CM and technical phase of proposing the VCC. Table 4 below lists the core challenges addressed in the
project with their proposed solutions.
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Irfansyah et al.
Current Trends
and Future Challenges
in Congestion Management
WiP Paper Practitioner-centered Logistics and Supply Chain Management in Crisis Response
Proceedings of the 17th ISCRAM Conference Blacksburg, VA, USA May 2020
Amanda Lee Hughes, Fiona McNeill and Christopher Zobel, eds.
Table 4. Challenges and Proposed Solutions in TIM-CM (Combined and adapted from CoSSaR (2017) and SAJOG
(2017))
No Challenge Solution
1 Both TIM and CM are complex multi-agency, multi-
jurisdictional activities that are interdependent yet with
distinct goals, methods, and stakeholders.
Create the appropriate TIM-CM
joint operations command
structure.
2 CM has less defined command structures and processes than
TIM, which is guided and driven by pre-existing operational
protocols and structures. A CM plan, cannot currently be
immediately prepared and launched because operators lack
enabling information, structures, processes, and policies.
Interagency Concept of
Operations (ConOps) supported
by VCC
3 Overcome information-sharing barriers across TIM-CM
agencies, especially between law enforcement and
transportation agencies.
Cloud-based Information
Sharing Environment (ISE)
across TIM and CM processes
that provides appropriate
security and access for handling
agency data.
4 Major highway incidents result in severe regional impacts
beyond the incident site. Managing incident-generated
congestion continues after the incident is cleared, involves a
more-diverse group of stakeholders, and covers not only a
greater portion of the freeway, but also the interconnected
arterials and alternate modes of transportation, as well as the
people, facilities and services that rely on transportation
infrastructure.
Congestion Analysis Engine and
pre-planned ICM options that are
triggered and implemented
through enhanced VCC
capabilities
5 Enhance TIM-CM communication with the public, and
engage commuters as stakeholders in the design of TIM-CM
enhancements, as well as better communication and
coordination with Metro and Sound Transit, the Port of
Seattle, private transportation systems, and ride-hailing
companies are needed. In addition, an effective means to
coordinate with regional employers to address incident
impacts on their employees is lacking.
Gather insight into current
Seattle commuter behaviors and
preferences and Enhanced
Public and Private Sector
Outreach supported by VCC
capabilities
6 CM information flow occurs primarily in a one-way hub-and-
spoke model with TIM stakeholders at the center.
Understandably, TIM focuses almost exclusively on
immediate life-saving response issues and pushes out
information to CM stakeholders as a secondary priority. CM
stakeholders lack access to law enforcement dispatch systems
and have limited access to on-scene responders, limiting their
ability to respond proactively to building congestion.
VCC that employs the ISE and
shared services to increase
desired information sharing
without distracting from urgent
responder priorities
3. Roles
This project, conducted by SAJOG, consists of nineteen entities of partners from public and private sectors that
are working together. Their roles are defined in the SAJOG charter. This group is led by six central public agencies
within Washington State as the core working group, namely WSDOT, SDOT, SPD, Seattle Fire Department
(SFD), King County Metro Transit (KCMT), and WSP. Meanwhile, other agencies, local partners, and private
partners joined SAJOG in the sub-working group (SAJOG 2017).
While the current target of the TIM-CM operation is in the city of Seattle and the surrounding regions that are
passed by the I-5 corridor, the involvement of the higher State of Washington is already included in the group.
Therefore, a change in public regulations or processes which potentially disrupt the operation of the project can
be minimized.
630
Irfansyah et al.
Current Trends
and Future Challenges
in Congestion Management
WiP Paper Practitioner-centered Logistics and Supply Chain Management in Crisis Response
Proceedings of the 17th ISCRAM Conference Blacksburg, VA, USA May 2020
Amanda Lee Hughes, Fiona McNeill and Christopher Zobel, eds.
4. Systems and Services
As already hinted on the proposed solutions in the previous chapter, there are several layers of systems and
services to articulate the operational needs of ICM. As shown in figure 6 below, there are four layers: an enhanced
information sharing architecture/environment, enhanced data sharing and shared capabilities, shared workflow
applications that support the ConOps, and the VCC and user experience layer (SAJOG 2017).
Although the technological development of the systems is on the hand of partners working in the sub -working
group, the agencies in the core-working group that will use the systems and the services first hand are
collaboratively designing the environment themselves. This approach is necessary to minimize issues in system
adoption by public agencies. In order to have a seamless connection between different systems that are currently
used by different agencies, TIM-CM will be continuously connecting or replicating those systems in ICM’s cloud
environment.
Figure 6. Layers of ICM (SAJOG 2017)
5. Deployment Methodologies
The deployment of VCC is supported by cutting-edge technology provided by the private partners in the group,
such as Amazon and Microsoft. The aim is to have a scalable and flexible system to accommodate the variability
of the traffic.
As shown in figure 7 below, the deployment methodologies are iterative and agile. This human-centered approach
links development and deployment concurrently to allow any issues to be addressed throughout the process.
Additionally, ongoing evaluation and improvement will be conducted iteratively. Emerging and future
technologies will be incorporated in the future to enrich the data and capabilities of the systems (SAJOG 2017).
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Irfansyah et al.
Current Trends
and Future Challenges
in Congestion Management
WiP Paper Practitioner-centered Logistics and Supply Chain Management in Crisis Response
Proceedings of the 17th ISCRAM Conference Blacksburg, VA, USA May 2020
Amanda Lee Hughes, Fiona McNeill and Christopher Zobel, eds.
Figure 7. Deployment of a VCC for ICM (SAJOG 2017)
NEW CHALLENGES IN CONGESTION MANAGEMENT
This chapter discusses the current and future evolution in transportation under two selected themes. Then,
challenges that are currently happening or will happen as the consequence of the evolution from literature are
listed and connected to the aspects of congestion.
Challenges due to Mobility Technology
In addition to the continuous research from the past in improvement of the current mobility technology, such as
increasing safety and reducing emission, one of the primary goal of future mobility technology is eliminating
human involvement in the form of Autonomous Vehicle (AV).
By eliminating human involvement, AV promise to eliminates some traffic accidents that are caused by human
error. This promise will directly reduce the non-recurrent instances of congestion. However, not all kinds of
accidents can be avoided by AV. When an instance of an accident is cannot be avoided, an ethical decision by the
behaviour of the AV will need to select which outcome it prefers (Bonnefon et al. 2016). This social dilemma,
such as sacrificing its passenger for the greater good of saving pedestrians, adds another aspect in the breakthrough
AV technology that needs to be convincing to users (Nikitas et al. 2017).
Future projection of the mobility environment is to have a machine-led environment in the form of connected AV
that will not only take over the driving function from humans but also having real-time synchronization with other
mobility actors (Nikitas et al. 2017). This connectivity is one additional promises on how machine -led mobility
could reduce congestion problems. The multidimensional environment of the AV needs multiple stakeholders to
control the real-time data supplied by the vehicle. Transportation management stakeholders consortium, such as
SAJOG from the case study in the previous chapter, could be the stakeholder that is given some degree of indirect
control of AV’s behaviour on the transportation network.
The transition from our current human-driven vehicles into AVs can lead to shared road space between them. This
mixed traffic situation could create more problems than current full human-led or future full machine-led mobility
(Nikitas et al. 2017).
The challenges discussed above are listed in table 5 below, with its connection to aspects of congestion.
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Irfansyah et al.
Current Trends
and Future Challenges
in Congestion Management
WiP Paper Practitioner-centered Logistics and Supply Chain Management in Crisis Response
Proceedings of the 17th ISCRAM Conference Blacksburg, VA, USA May 2020
Amanda Lee Hughes, Fiona McNeill and Christopher Zobel, eds.
Table 5. Challenges from Mobility Technology
No Challenge Relation to Congestion
1 Ethical decisions of AV (Bonnefon et al. 2016) Driver behaviour (source) and
Traffic incidents (cause)
2 Convincing new technologies to users (Nikitas et al. 2017) Travel behaviour (source)
3 Better decision-making (Nikitas et al. 2017) Travel behaviour (source) and
Driver behaviour (source)
4 Mixed traffic situations (Nikitas et al. 2017) Traffic incidents (cause) and
Driver behaviour (source)
Challenges from Social Evolution
In the context of business, the development of Information and Communication Technologies (ICT) is changing
how transportation infrastructure is used. One of the primary examples is that ICT enables broad remote activities
and therefore reduces the number of trips needed to conduct business. Schuckmann et al. (2012) list two future
projections regarding social perspective on transportation infrastructure; firstly, transportation infrastructure is
still an essential service of economics but no longer a deciding factor in the competition for investment. Secondly,
the infrastructure of ICT is becoming a stronger driver of economic growth compared to physical transportation
infrastructure. These projections show how the business sector might change their perspective on transportation
infrastructure in the future.
However, city centers with higher density mean that more users are competing for limited spaces and roads
(Sanchez-Diaz and Browne 2018). Together with the advancing e-commerce, the increasing need for goods
distribution results in traffic congestion as one of the externalities (Arnold et al. 2017). Several solutions to reduce
traffic congestion from goods distributions to shopping centers were already proposed and implemented. Off-peak
deliveries and centralized receiving stations are some of the solutions that can be deemed effective in reducing
the effect (Dalla Chiara and Cheah 2017). On the other hand, e-commerce’s home deliveries tighter and
unpredictable schedule would further tighten traffic capacity.
In the context of urban living, Lund et al. (2017) lay several future social constructs that will affect transportation
management directly or indirectly, which are:
(1) increased densification of city centers,
(2) changes in the cost of owning a car,
(3) sharing economy or service is getting more acceptance
(4) liveability is more attractive,
(5) private car is no longer a status symbol, and
(6) people are always connected (Lund et al. 2017).
The usage of personal transportation is changing from having a physical car into having access to a car in the form
of Mobility as a Service (MaaS). MaaS offers an integrated public mobility solution with similar convenience to
the private car (Jittrapirom et al. 2017). Simply put, getting a taxi is changing from having more public
transportation attributes (such as waiting for one on the side of the street) to more private transportation (available
at any time on a click).
Most challenges in implementing new social aspects in a transportation system lie in security and regulation. The
success of transportation platforms such as Uber and DriveNow over conventional taxi shows that acceptance by
the prospective user is not a big challenge. Furthermore, some of these platforms, such as Grab and Go-Jek, are
evolving into super apps that are able to cater many aspects of the future social construct. Table 6 lists several
challenges in transportation caused by the changing of the social construct that can be related to congestion from
literature.
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Irfansyah et al.
Current Trends
and Future Challenges
in Congestion Management
WiP Paper Practitioner-centered Logistics and Supply Chain Management in Crisis Response
Proceedings of the 17th ISCRAM Conference Blacksburg, VA, USA May 2020
Amanda Lee Hughes, Fiona McNeill and Christopher Zobel, eds.
Table 6. Challenges from Social Evolution
No Challenge Relation to Congestion
1 Unification or integration of future services (e.g., MaaS) to
existing services (Nikitas et al. 2017)
Excess demand (cause) and
Travel behaviour (source)
2 Different existing policy to implement a full-scale solution
(Wosskow 2014)
Travel behaviour (source)
3 Evolution of parcel distributions from B2B to the tighter and
unpredictable B2C (Nemoto et al. 2001, Arnold et al. 2017)
Excess demand (cause) and
Travel behaviour (source)
CONCLUSION
This research explored and discussed the following topics: the definition and aspects of congestion, the current
trend with a case study of integrated solutions in managing congestion, and future challenges in CM caused by
two selected factors. The case study is set as an example of how different agencies and sectors, together as the
stakeholders of transportation, are able to work collaboratively to propose a joint solution that can help managing
congestion.
The absence of a systematic approach in reviewing the literature is one of the limitations in this paper. ECMT
(2017) was used as a source in building the initial formation of the supporting literature that were mainly obtained
from Google Scholar and Scopus. Subsequently, an iterative approach that adds more queries from the collected
literature was applied. A systematic methodology, such as selecting specific years of publication or journal
databases, can be considered as an enhancement to this research. The next limitation is the scope of the case study,
TIM-CM, as it only focuses on some parts of CM measures in managing congestion.
Further research can enhance this study by comparing several projects of IS-based CM measures similar to TIM-
CM from different regions. This comparison can be used to set a standard of the systems and services organization
that is required to manage similar congestion problems. Another research agenda is to conduct a literature study
systematically to generate a complete and concise outlook of new challenges of future congestion. The outlook is
expected to assist practitioners and academics in preventing future congestion by producing well-adapted CM
measures.
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