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Kagho, Hensle, Balac, Freedman, Twumasi-Boakye, Broaddus,
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Demand Responsive Transit Simulation of Wayne County, Michigan 1
2
Grace O. Kagho 3 Institute for Transport Planning and Systems
(IVT) 4
ETH Zurich, Stefano-Franscini-Platz 5, 8093, Zurich, CH,
Switzerland 5
E-mail: [email protected] 6
7
David Hensle 8 Analyst 9
RSG 10
1515 SW 5th Ave #1030, Portland, OR 97201 11
Email: [email protected] 12
13
Milos Balac 14 Institute for Transport Planning and Systems
(IVT) 15
ETH Zurich, Stefano-Franscini-Platz 5, 8093, Zurich, CH,
Switzerland 16
E-mail: [email protected] 17
18
Joel Freedman 19 Senior Director 20
RSG 21
1515 SW 5th Ave #1030, Portland, OR 97201 22
Email: [email protected] 23
24
Richard Twumasi-Boakye 25 Research Scientist 26
Ford Motor Company 27
2101 Village Rd., Dearborn, MI. 48124 28
Email: [email protected] 29
30
Andrea Broaddus 31 Senior Research Scientist 32
Research & Advanced Engineering, Mobility & Robotics
Department 33
Ford Greenfield Labs, Palo Alto, California, 94303 34
Email: [email protected] 35
ORCID: 0000-0003-3175-5986 36
37
James Fishelson 38 Supervisor, Mobility Research 39
Ford Motor Company 40
2101 Village Rd., Dearborn, MI. 48124 41
Email: [email protected] 42
43
Kay W Axhausen 44 Institute for Transport Planning and Systems
(IVT) 45
ETH Zurich, Stefano-Franscini-Platz 5, 8093, Zurich, CH,
Switzerland 46
E-mail: [email protected] 47
48
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50 Word Count: 6746 + 3 tables = 7496 words 51
mailto:[email protected]
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1
2
Submitted: August 1, 2020 3
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ABSTRACT 1
Demand Responsive Transit (DRT) can provide an alternative to
private cars and complement existing 2
public transport services. DRT’s potential is enhanced by the
advent of automation; such services are often 3
referred to as shared autonomous vehicles (SAVs). However, the
successful implementation of DRT 4
services remains a challenge; as both researchers and policy
makers can struggle to determine what sorts 5
of places or cities are suitable for it. Research into
car-dependent cities with poor transit accessibility are 6
sparse. In this study, we address this problem, investigating
the potential of DRT service in Wayne County, 7
USA, whose dominant travel mode is a private car. Using an
agent-based approach, we simulate DRT as a 8
new mobility option for this region, thereby providing insights
on its impact on operational, user, and 9
system-level performance indicators. We test DRT scenarios for
different fleet sizes, vehicle occupancy, 10
and cost policies. The results show that a DRT service in Wayne
County has certain potentials, especially 11
to increase the mobility of lower-income individuals. However,
we also show that introducing the service 12
may slightly increase the overall VKT. Specific changes in
service characteristics, like service area, pricing 13
structure, or preemptive relocation of vehicles, might be needed
to fully realize the potential of pooling 14
riders in the proposed DRT service. We hope that this study
serves as a starting point for understanding the 15
impacts and potential benefits of DRT in Wayne County and
similar low-density and car-dependent urban 16
areas, as well as the service parameters needed for its
successful implementation. 17
18
Keywords: Demand Responsive Transit (DRT), Shared Mobility,
Agent-based Models, Shared 19
Autonomous Vehicles 20
INTRODUCTION 21
Mobility is important for ensuring people have access to daily
needs. Today, the necessity to move, 22
coupled with population growth and economic development in urban
areas have led to increased congestion. 23
Clearly, we need more sustainable transport options to move
people safely through cities. Fixed-route and 24
timetable-based public transit (PT) present an effective
solution in areas with high demand and well-utilized 25
corridors; however, many locations – including cities, report
underperforming public transit services, not 26
sufficient for serving all travelers due to low and sparse
demand (1). This leaves private cars as the 27
predominant alternative for commuters. 28
While not novel, in recent years, shared mobility has become a
viable transportation option. This 29
is partly due to the diffusion of information and communication
technologies used in developing systems 30
for requesting trips and making payments within a single
software platform. In this paper, we focus on 31
Demand Responsive Transit (DRT). DRTs fall under the broader
category of shared mobility and comprise 32
services such as, taxis, paratransit, microtransit, etc. They
refer to a type of quasi-public transport that 33
allows vehicles to modify their routes based on service demand
(2). Operationally different from fixed route 34
public transit, DRTs permit vehicles to pick-up and drop-off
passengers at locations of their choice (3). 35
When carefully designed, DRTs can complement existing public
transit. However, there remains the need 36
to investigate how these services will perform amid existing
transport modes to improve mobility. Few 37
studies focus on understanding this need, particularly in areas
with low PT utilization and significant 38
socioeconomic disparities, such as Wayne County, Michigan.
39
Private cars are the dominant mode of travel in the City of
Detroit and Wayne County, with PT 40
accounting for less than 1% of total trips. This is largely
because of high auto ownership (~95% of 41
households have access to at least one auto). Additionally,
transit access is limited and inconvenient for trip 42
making. This presents a problem for many residents who may
struggle to afford high auto insurance rates 43
(4). Wayne County is home to the “big three” U.S. auto makers,
Ford, Chevrolet, and General Motors. The 44
local stakeholders have been discussing possibilities for
expanding transit and integrating new on-demand 45
mobility options as they anticipate socioeconomic development in
the area due to auto industry employment 46
growth and future autonomous vehicle production. Persistent
socioeconomic disparities (5) as well as low 47
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PT ridership make Wayne County a unique location to model DRTs
as a possible solution for improving 1
mobility, and will provide valuable insights to transportation
agencies and researchers on DRT operations 2
in similar cities. 3
For this reason, we model and simulate a hypothetical DRT
service in Wayne County, Michigan. 4
In this context, we define DRT as a shared fleet of vehicles
with operational tolerance for pooling, and with 5
travelers picked-up and dropped-off at their desired locations..
This paper contributes to the state of art by 6
developing a novel computational schema to convert a trip-based
travel demand model into inputs for 7
developing a calibrated agent-based model in MATSim, an
open-source mobility simulation platform with 8
an integrated DRT module. This required a further step of
developing and calibrating a mode choice model 9
to estimate demand for the DRT service. 10
The objective of this paper is to understand the demand
potential of DRT for Wayne County based 11
on fleet size, cost and vehicle capacity factors. We ran a set
of DRT scenarios with varying levels of these 12
factors as a method of demand estimation and to understand the
impact of DRT on operational, user, and 13
system-level performance indicators. For the effectiveness of
the designed DRT, we try to answer the 14
following questions: What is the demand for the new service and
how will this affect fleet size and vehicle 15
utilization? How does DRT fare affect demand? How do
service-design parameters affect user experience 16
in terms of wait time and total trip time due to detour
allowances? How will the DRT service impact 17
mobility in Wayne County in terms of system-level vehicle
kilometers travelled (VKT)? 18
The remainder of this paper is organized as follows. Section 1
provides background context and 19
review of pertinent literature regarding agent-based modelling
and DRT. Section 2 describes the research 20
methodology, demand and supply models, and scenario design for
the integrated DRT module. Section 3 21
describes the results, and Section 4 provides a discussion of
the research findings. Finally, we present the 22
conclusion of this paper and highlight areas for future work.
23
24
BACKGROUND 25
The past decade has seen an explosion in DRT and related shared
automated vehicle (SAV) 26
research and modelling, where vehicles serve passenger demand
with both spatial and temporal flexibility 27
as opposed to the fixed routes and schedules of traditional
transit (6). An overall review of emerging 28
mobility on demand’s operational concepts is given by Shaheen et
al. (7), and Narayanan (8) provides a 29
more focused review of different studies on SAVs. 30
The most general approach comes from “aggregate models,” which
use combinations of raw data, 31
assumptions, and equations or assumed relationships, to
deterministically estimate system-level 32
performance metrics: costs, time, and more. For example,
Greenblatt and Saxena, (8) use existing taxi data 33
as a starting point, adding assumptions about the performance of
electric SAVs to estimate the national 34
effects on greenhouse gas emissions. Zachariah et al take a more
detailed and hybrid approach, using a 35
network assignment model to develop a time-based trip schedule
for all passengers (9). More detailed are 36
“Network Assignment Models,” where traditional travel demand
modeling tools (e.g. TransCAD) are used 37
with DRT added as an existing mode (10). They are strong at
optimization, such to test different 38
optimization strategies to relocate vehicles when not in use
(11,12). However, being macroscopic in scope, 39
these assignment models lack the fidelity to model the actual
performance of a DRT system, such as 40
independent pick-ups and drop-offs. 41
The third and most detailed group, “Agent-Based Models” (ABMs)
directly model the behavior of 42
SAVs as individual agents in a DRT simulation environment,
including at a minimum passenger pick-up, 43
drive time, drop-off, and behavior while empty. Various sharing
and relocation algorithmic approaches are 44
often also simulated. These models have often been used to
investigate the theoretical question of how 45
many SAVs would be required to achieve the same level of
mobility as private vehicles, finding 46
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replacement ratios ranging from approximately 2-40, (i.e.
private vehicles replaced by one SAV) (9). This 1
level of detail makes ABMs the most flexible of the model
groups, useful for testing a range of hypothetical 2
situations, such as different service types (13), service areas
(14), approaches to ride pooling (15-17), 3
vehicle relocation and staging strategies (18-20), impacts of
traffic assignment (21), and cost impacts of 4
different scenarios (22). Note that many researchers have a
combined approach, utilizing a network 5
assignment model to first generate and assign demand to yield a
spatial origin-destination matrix and 6
roadway travel speed skims, and then employ an ABM to estimate
vehicle movements and passenger 7
interactions (11,23,24). Of the ABMs, MATSim is one of the most
popular modeling tools, having the 8
benefit of being both open-source and activity based. 9
Getting good estimates of travel demand is an omnipresent
challenge; one of the reasons that so 10
many studies focus on New York, Singapore, and Austin is that
they have publicly available taxi or TNC 11
data. No studies to date have specifically focused on overall
DRT performance in a city like Detroit, which 12
is characterized by low-density, car-dependent urban structure,
and relatively low levels of traffic 13
congestion. Similarly, relatively few studies have tried to
endogenously model mode choice within an 14
ABM. The network models of Chen et al., Childress et al., and
Gucwa (15,19,25) model mode choice 15
exogenously as part of the traditional four-step transportation
forecasting process, using multinomial logit 16
models considering monetary costs, wait times, and in-vehicle
travel times. Liu et al., (20) extend an 17
existing ABM model of Austin (19) to consider varying per-km
costs jointly with mode choice. 18
Other researchers consider mode choice endogenously in their
models, as done in this paper. For 19
example, Hörl et al. (26) treats mode choice directly as part of
their ABM, with fully specified mode choice 20
utility functions, showing the potential for automated taxis to
serve up to 60% of all trips in their 21
hypothetical Sioux City network. Azevedo et al. (27) models the
choice between private vehicles, public 22
transit, and SAVs, and argues that factors affecting mode choice
could also affect auto purchase decisions, 23
though they do not explicitly include this possibility in the
model. Zhang (28) shows that SAVs could take 24
mode share from transit, especially for shorter trips with low
in-vehicle travel times. Moreno et al., (29) 25
examines how the shift to DRT could affect overall number of
trips and distance traveled. 26
METHODOLOGY 27
28
Overview of MATSim 29
MATSim (30) is an extendable, multi-agent simulation framework
implemented in Java. It 30
simulates travelers’ – referred to as agents – activities and
trips on a network for an entire day. Agents in 31
MATSim have daily plans, which consist of their activity chains
and socioeconomic information. Each 32
activity contains certain information such as activity location,
start, and end time. In a typical application, 33
MATSim operates in an iterated loop until it achieves user
equilibrium. It uses a co-evolutionary 34
algorithm where each agent optimizes its individual plan until
the system converges to a stable state. 35
In this novel application, we combine demand from an existing,
calibrated trip-based model for 36
Southeast Michigan with the trip assignment capabilities of
MATSim. Since we are modeling individual 37
trips made by agents, rather than agents’ daily activity plans,
the MATSim utility scoring mechanism 38
which adjusts activity plans is not used to achieve system
equilibrium. Only route choice is optimized to 39
achieve convergence, given fixed origins, destinations, and
departure times. We integrate a mode choice 40
model to predict choice of private auto, public transport, walk,
bike, and DRT modes. Below we discuss 41
the model network and demand components in more detail. 42
Network Creation 43
All roads inside Wayne County, Michigan were extracted from
OpenStreetMap to form the basis 44
of the MATSim network. To reduce computational complexity, the
road network was thinned outside of 45
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the downtown Detroit area to match the planning network used in
the Southeast Michigan Council of 1
Governments (SEMCOG) E-7 trip-based model (31). Routing issues
arising from the network thinning 2
procedure were manually corrected. 3
MATSim requires links to be only unidirectional, so, two-way
links from OpenStreetMap were 4
duplicated to create two identical links, but with the start and
end nodes switched. Link capacities and free-5
flow speeds (when not present in the OpenStreetMap network) were
calculated based on the SEMCOG E-6
7 model network. 7
Transit routes were created from General Transit Feed
Specification (GTFS) data, where available, 8
or coded manually. Transit operators in the model include the
Suburban Mobility Authority for Regional 9
Transportation (SMART) and Detroit Department of Transportation
(DDOT) buses servicing downtown 10
and surrounding suburbs, and the QLINE streetcar and Detroit
People Mover automated light rail system 11
servicing the central business district. We used the pt2matsim
software package (32) to combine GTFS 12
data for these services and to create additional links in cases
where routes extended beyond the Wayne 13
County network. This package also created a public
transportation vehicle list and schedules. 14
15
Figure 1 MATSim network of Wayne county 16
Travel Demand 17
The SEMCOG E-7 trip-based model was used as the base travel
demand for this work. It contains 18
more than 20 million person trips across six counties, 2899
travel analysis zones, 8 trip purposes, and 15 19
trip modes. Transforming the E-7 trips into MATSim “agents”
required two steps. First, SEMCOG E-7 20
trip tables were disaggregated into individual trips that could
equate to MATSim agents. Secondly, since 21
the SEMCOG model encompasses a much larger area than the
modeling area of Wayne County used for 22
this project, the trips/agents were filtered to select only
those that traversed our network for inclusion in the 23
DRT simulation. 24
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For the first step, Production-Attraction (PA) matrices from the
E-7 mode choice model were first 1
converted into OD format. The E-7 mode choice model creates
Production-Attraction (PA) matrices by 2
income group, trip purpose, and peak or off-peak travel period.
Each PA matrix contains a core for each of 3
the 15 available trip modes in the E-7 model. PA matrices were
converted into Origin-Destination (OD) 4
format using the PA to OD factors specified in the SEMCOG E-7
model. PA to OD factors are dependent 5
on trip purpose, time of day, and direction. Peak period PA
matrices were split into AM and PM whereas 6
the off-peak matrices were split into midday, evening, and night
for a total of five time-of-day periods. 7
After conversion to OD format, the shared ride 2 and shared ride
3+ trip tables were divided by 2 8
and 3.5, respectively, to convert from person trips to single
vehicle MATSim agents. All cells in the OD 9
matrix were then converted to an integer based on Monte Carlo
sampling. Matrices for commercial vehicle 10
trips already in OD format were also integerized and included.
These commercial vehicles, which represent 11
light, medium, and heavy trucks are included in the simulation
as the background traffic because they 12
contribute to network congestion. 13
For the second step, to select which trips entered or exited the
region, we coded and then skimmed 14
unique link identifiers to determine whether a trip crossed the
model boundary, and if so, over which link 15
and in which direction. Trips that had the entirety of their
journey outside of the modeling area were 16
excluded. Origins and destinations outside the model region were
coded to out network’s boundary link. 17
After completion of this process, the resulting demand of about
9 million trips within the modeled 18
area can be seen in Table 1, segmented by trip mode type and
time of day. Each of these trips was then 19
input to MATSim as an “agent” making only one trip. 20
Table 1 Trips By Mode and Time of Day 21
Time of Day Period
Trip Mode AM MD PM EV NT All
Commercial Vehicle 108,845 615,406 145,369 38,165 38,032
945,817
Non-Motorized 155,141 308,366 257,522 76,880 - 797,909
Single Vehicle 1,032,462 1,813,018 2,324,005 1,229,201 705,438
7,104,124
Transit 15,225 27,917 22,517 8,107 - 73,766
All 1,311,673 2,764,707 2,749,413 1,352,353 743,470
8,921,616
22
Next, every MATSim agent was assigned a trip start time based on
the time-of-day period they 23
originated from. Start times were sampled from the time-of-day
distribution of counts provided in the E-7 24
model documentation. Linear interpolation was used to distribute
the half-hour count data to one second 25
resolution to avoid bunching on the network due to multiple
MATSim agents spawning in the same location 26
at the same time. 27
All MATSim agents were also assigned network links for start and
end locations. Trip ends inside 28
the modeled area were allocated to MATSim network links using
Monte Carlo simulation based on link 29
length (longer links were more likely to be selected than
shorter links). Links corresponding to freeways 30
and freeway ramps were excluded from the sampling subset to
avoid agents starting or ending on highways. 31
As noted above, if the agent entered or exited the modeling
region, the start or end link corresponded to the 32
appropriate link crossing the boundary. 33
Finally, each agent was assigned attributes for use in a mode
choice model. Household income 34
attribute was given based upon the PA matrix the trip originated
from. Auto ownership attribute was given 35
based upon a distribution derived from Wayne County Census data
for each income category. Commercial 36
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vehicle agents and external trips were not given these
attributes, and were excluded from the subsequent 1
mode choice model. 2
Mode Choice Model 3
In this work, a discrete mode-choice (DMC) extension of MATSim
was used to simulate agents’ 4
mode choice decisions. The extension enables one to utilize
traditional discrete mode-choice models within 5
a MATSim simulation. It can consider trip or tour-based
constraints and mode availability rules. An 6
estimator assigns utility for each transport mode based on
travel costs and other travel characteristics. A 7
selector component defines how the alternative modes are chosen.
Detailed description of the extension can 8
be found in (33, 34). In the DMC extension of MATSim, only a
randomly sampled portion of the population 9
in any given iteration performs mode choice. For this study, in
each iteration, we sample 10% of the agents 10
to perform mode and route choice decisions. 11
Furthermore, we use a multinomial logit model for mode choice.
The choice model formulation is 12
given in Equations 1 to 5 while the model parameters are
specified in Table 2. The utility 𝑈𝑖, (𝑖 = car, pt, 13 walk, bike,
DRT) is calculated per mode, based on agents’ income and auto
availability attributes. 𝑘, 14 represents income level, (Low
income, Middle-Low income, Middle High & High); while 𝑙
represents auto 15 ownership levels, (No Autos, Some Autos, All
Autos), as depicted in Table 2. Choice variables are denoted 16
as 𝑥 while 𝛽 and 𝛼 denote marginal utility parameters and
alternative specific constants (ASC) respectively. 17 The mode
choice variables include in-vehicle travel time, out-of-vehicle
travel time (wait time and 18
access/egress time), and travel cost. The parameters in the
model are taken from the SEMCOG mode choice 19
model. Travel times and costs are provided by the MATSim network
router based on the trip origin, 20
destination, and departure time. Note that we do not consider
the cost of parking in this model. However, 21
parking is plentiful and free in most of Wayne County outside of
downtown Detroit. 22
We apply parameters for public transit to the DRT mode (Equation
5) since the SEMCOG model 23
does not consider DRT. The DRT service is available only for
internal trips, that is, those trips that originate 24
and end in Wayne County. This is due to the fact that we do not
know where external trips in the model 25
start or end. Furthermore, since a door-to-door DRT scheme is
applied, there is no access or egress time as 26
for the public transport. 27
𝑈𝑐𝑎𝑟,𝑘,𝑙 = 𝛼𝑐𝑎𝑟,𝑖𝑛𝑐𝑜𝑚𝑒,𝑘 + 𝛼𝑐𝑎𝑟,𝑎𝑢𝑡𝑜𝑠,𝑙 + 𝛽𝑐𝑎𝑟,𝑡𝑟𝑎𝑣𝑒𝑙𝑇𝑖𝑚𝑒 .
𝑥𝑐𝑎𝑟,𝑡𝑟𝑎𝑣𝑒𝑙𝑇𝑖𝑚𝑒 + 28
𝛽𝑐𝑜𝑠𝑡, 𝑖𝑛𝑐𝑜𝑚𝑒,𝑘 . 𝑥𝑐𝑎𝑟,𝑐𝑜𝑠𝑡 (1) 29
𝑈𝑝𝑡,𝑘,𝑙 = 𝛼𝑝𝑡,𝑖𝑛𝑐𝑜𝑚𝑒,𝑘 + 𝛼𝑝𝑡,𝑎𝑢𝑡𝑜𝑠,𝑙 + 𝛽𝑝𝑡,𝑡𝑟𝑎𝑣𝑒𝑙𝑇𝑖𝑚𝑒 .
𝑥𝑝𝑡,𝑡𝑟𝑎𝑣𝑒𝑙𝑇𝑖𝑚𝑒 + 𝛽𝑝𝑡,𝑤𝑎𝑖𝑡𝑇𝑖𝑚𝑒 . 𝑥𝑝𝑡,𝑤𝑎𝑖𝑡𝑇𝑖𝑚𝑒 +30
𝛽𝑝𝑡,𝑎𝑐𝑐𝑒𝑠𝑠/𝑒𝑔𝑟𝑒𝑠𝑠𝑇𝑖𝑚𝑒 . 𝑥𝑝𝑡,𝑎𝑐𝑐𝑒𝑠𝑠/𝑒𝑔𝑟𝑒𝑠𝑠𝑇𝑖𝑚𝑒 + 𝛽𝑝𝑡,𝑐𝑜𝑠𝑡,
𝑖𝑛𝑐𝑜𝑚𝑒,𝑘 . 𝑥𝑝𝑡,𝑐𝑜𝑠𝑡 (2) 31
𝑈𝑤𝑎𝑙𝑘,𝑘,𝑙 = 𝛼𝑤𝑎𝑙𝑘,𝑖𝑛𝑐𝑜𝑚𝑒,𝑘 + 𝛼𝑤𝑎𝑙𝑘,𝑎𝑢𝑡𝑜𝑠,𝑙 + 𝛽𝑤𝑎𝑙𝑘,𝑡𝑟𝑎𝑣𝑒𝑙𝑇𝑖𝑚𝑒 .
𝑥𝑤𝑎𝑙𝑘,𝑡𝑟𝑎𝑣𝑒𝑙𝑇𝑖𝑚𝑒 (3) 32
𝑈𝑏𝑖𝑘𝑒,𝑘,𝑙 = 𝛼𝑏𝑖𝑘𝑒,𝑖𝑛𝑐𝑜𝑚𝑒,𝑘 + 𝛼𝑏𝑖𝑘𝑒,𝑎𝑢𝑡𝑜𝑠,𝑙 + 𝛽𝑏𝑖𝑘𝑒,𝑡𝑟𝑎𝑣𝑒𝑙𝑇𝑖𝑚𝑒 .
𝑥𝑏𝑖𝑘𝑒,𝑡𝑟𝑎𝑣𝑒𝑙𝑇𝑖𝑚𝑒 (4) 33
𝑈𝑑𝑟𝑡,𝑘,𝑙 = 𝛼𝑑𝑟𝑡,𝑖𝑛𝑐𝑜𝑚𝑒,𝑘 + 𝛼𝑑𝑟𝑡,𝑎𝑢𝑡𝑜𝑠,𝑙 + 𝛽𝑑𝑟𝑡,𝑡𝑟𝑎𝑣𝑒𝑙𝑇𝑖𝑚𝑒 .
𝑥𝑑𝑟𝑡,𝑡𝑟𝑎𝑣𝑒𝑙𝑇𝑖𝑚𝑒 + 34
𝛽𝑑𝑟𝑡,𝑤𝑎𝑖𝑡𝑇𝑖𝑚𝑒 . 𝑥𝑑𝑟𝑡,𝑤𝑎𝑖𝑡𝑇𝑖𝑚𝑒 + 𝛽𝑑𝑟𝑡,𝑐𝑜𝑠𝑡, 𝑖𝑛𝑐𝑜𝑚𝑒,𝑘 . 𝑥𝑑𝑟𝑡,𝑐𝑜𝑠𝑡
35
(5) 36
37
38
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9
1
2
Table 2 Calibrated Parameters for Mode Choice Model 3
4
5
Simulation of Demand Responsive Transit (DRT) 6
DRT simulations for Wayne County are conducted using a DRT
extension (35) of MATSim. The 7
DRT extension has a dispatching algorithm managing the movement
of DRT fleet and travelers’ requests. 8
This process, like in real life, is dynamic and depends on the
state of the simulation system. An agent 9
choosing the DRT service, submits a trip request and waits for a
vehicle. The dispatcher then assigns a 10
vehicle with the smallest detour time loss, to handle the
request. Detour time loss is a measure of detouring 11
due to adding additional passengers to a vehicle, which consists
of pick-up detour time, drop-off detour 12
time, and stop duration for pickup and drop off. It is added to
the total travel time of agents with shared 13
rides. If there are more trip requests than vehicles in the
system, it is possible that some agents cannot be 14
served within pre-defined wait time. In that case these agents’
trips will be labelled as "rejected", and then 15
removed from the micro-simulation of the current iteration. When
a DRT vehicle drops off its last passenger 16
and there are no other trip requests at that particular time,
the DRT vehicle stays at the location where the 17
last passenger has been dropped off. 18
The DRT module requires some configuration settings which define
the vehicle fleet and 19
operational parameters. Vehicles are generated based on fined
fleet size, maximum vehicle capacity, and 20
start and end locations. We set a maximum customer wait time and
detour time of 15 minutes each and a 21
Trip Mode Low
Income
Middle-
Low
Income
Middle-
High &
High
Income
No
Autos
Some
Autos
(0
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10
stop duration of 105 seconds (1.75 minutes). Finally, we set the
start time for the simulation at 12 AM and 1
end time at 4:00 PM. A total of approximately 5.4 million trips
are simulated over this period. 2
3
DRT Service Level Scenarios 4
The objective of this paper is to understand the demand
potential of DRT for Wayne County based 5
on fleet size, cost and vehicle capacity factors. Therefore we
defined 16 DRT scenarios consisting of four 6
different vehicle fleet sizes (100, 250, 500 and 1000), two
different vehicle capacities (4 and 7 occupants) 7
and two different fare rates ($2 and $4). Vehicle capacity had
no influence on the results, hence analysis 8
are presented for 4 occupancy vehicles. 9
10
RESULTS 11
In this section, we compare the different scenarios and analyze
the impact of different fleet sizes, 12
vehicle sizes, and fares on, DRT demand, operational performance
indicators, and system performance. 13
First we looked at three operational performance indicators,
fleet size and fare level, vehicle kilometers 14
travelled (VKT), and vehicle occupancy. This provides insights
on DRT utilization and an understanding 15
of minimum requirements for a feasible DRT service in Wayne
County. Next we looked at how the DRT 16
service parameters (wait time, detour time) affect two measures
of customer experience; waiting time and 17
affordability. Finally, we look at the system impact of DRT in
Wayne County by comparing the baseline 18
scenario to the DRT scenarios. 19
Fleet size and fare level 20
The first scenario runs, were sensitivity tests for the minimum
fleet size required to serve demand 21
at each fare level. Results are shown in Figure 2, with demand
at the $2 and $4 levels shown with lines, and 22
served/rejected trips shown with bars. In MATSim, the DRT demand
is the total number of agents 23
requesting the DRT service, whether served or not; it is the sum
of actual DRT rides and rejections. Total 24
rejected rides are quite high with a small fleet, but are
reduced substantially as more vehicles are added to 25
the fleet. 26
As can be seen in the figure, increasing fleet size (i.e.
supply) results in a minimal change in the 27
total demand, observable as a downward trendline. However, a
100% increase in the price from $2 to $4 28
strongly reduces the demand by about 50%. At the $2 fare level,
DRT trips keep increasing with fleet size, 29
with the rate of change in rides per fleet size increase as 50%,
35%, and 18% respectively. This is not the 30
case at the $4 fare level. Between 100 and 250 fleets, there is
an increase of 26%, as the fleet size doubles, 31
this increase drops to 13% and finally a second doubling from
500 to 1000 fleet leads to a minimal increase 32
of 7%. The DRT trip rejections follow a similar trend. 33
To explain the almost stable and slightly decreasing demand, one
must look at how mode choice is 34
simulated in our adapted MATSim model, and the possible impact
of DRT vehicles on congestion in the 35
network. Besides the maximum waiting time and maximum detour
time, other parameters of the service 36
reliability (such as trip rejections) are not taken into account
in the mode choice model. Consequently, if in 37
a certain area, the demand is high and a lot of people are not
served, in the next iteration, agents’ decision 38
to use DRT service will not be influenced by rejection effect of
the previous iteration. This would explain 39
the reason why there is almost the same number of requests even
for a fleet size of 100 whose rejection rate 40
is about 80%. 41
The decreasing trend of the demand, on the contrary, possibly
reveals the impact of additional DRT 42
vehicles on the network. Even though the maximum fleet size of
1000 contributes to less than 0.1% of the 43
vehicles on the network, the actual usage of the fleet may have
impacted congestion in certain high demand 44
areas. Thus, increase in travel time is fed back to the mode
choice model, and influences agents’ decisions 45
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11
not to choose DRT service. Another explanation for the decrease
in demand could be due to stochastic 1
nature of the mode choice model. This suggests a need to apply
different random seeds in future work to 2
better understand these phenomena. 3
4
Figure 2: DRT demand (Requests, Rides, Rejections) 5
Vehicle kilometers travelled 6
Average total distance per DRT vehicle decreases as fleet size
increases. At the $2 fare level, it 7
ranged from 483 km per DRT vehicle for 100 fleet, to 188 km for
1000 fleet; similarly, 435km and 98km 8
for a $4 fare. Even though there are more vehicles serving more
trips, the overall average VKT per vehicle 9
reduces as fleet size increases. 10
Also, as fleet size increases, the ratio of passenger-less (i.e.
empty) to passenger VKT decreases 11
(the ratios are 0.32, 0.26, 0.19, 0.15, and 0.33, 0.25, 0.19,
0.15 for $2 and $4 fare respectively). From Figure 12
4 one can clearly see that with larger fleets, the percentage of
empty vehicles driving around reduces, as 13
more vehicles spread throughout the network. This effect is the
same for both fares except for the magnitude 14
of demand. We will also see this trend reflected in the reduced
wait times in Figure 6. 15
Furthermore, compared to an average trip distance of 10km for
car travel, the majority of the 16
passengers are using DRT for relatively shorter trips with
average trip distances between 5 and 7km. 17
18
Vehicle occupancy 19
Figure 3 shows little actual ridesharing in any of the scenarios
tested. This is also clearly seen in 20
Figure 4 which shows that most of the DRT trips throughout the
16hr day period are single occupancy trips. 21
This may be due to the fact that the model area includes a large
suburban portion of Wayne County 22
characterized by low density development, which makes it
challenging to pool rides. The highest average 23
occupancy per vehicle achieved in the simulations (1.12) was
less than the smallest vehicle capacity tested 24
(4 passengers). As a consequence, vehicle capacity had no
visible effect on the results. 25
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Kagho, Hensle, Balac, Freedman, Twumasi-Boakye, Broaddus,
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12
It is interesting to note that even with the high percentage of
idle vehicles throughout the day for 1
fleet size of 500 and 1000, these vehicles still reject agents’
requests, likely because of the pre-defined 2
service parameters, maximum wait time and detour time, and the
consequence of modelling a large region. 3
Since there is no rebalancing between trips, the vehicles are
sparsely spread in all of Wayne County, as they 4
pick up and drop off passengers to their destination. So even if
vehicles are not in use, rejections can still 5
happen, as the requests may be too far away from where the
vehicle is sitting and are unable to meet the 6
detour constraints for in-vehicle passengers and the wait time
constraint of the requesting passengers. There 7
is need to optimize the pooling, by testing different detour
factors and wait times in future scenarios as well 8
as limiting the DRT service to specific areas with high demand.
9
10
11
12
Figure 3: Vehicle Distance traveled by Occupancy 13
14
15
16
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13
1
2 Figure 4: Vehicle Occupancy by time of day (a: $2 fare, b: $4
fare, note: stay means idle time) 3
4
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Kagho, Hensle, Balac, Freedman, Twumasi-Boakye, Broaddus,
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14
Passenger rides and wait time 1
We have already seen more agents making requests for the DRT
service than the service can meet. 2
Figure 5 shows the hourly distribution of the DRT passenger
rides. The passenger rides which are the 3
accepted requests, are higher in the morning peak period between
6am and 8am. Afterwards, there is a 4
slight decrease that spreads throughout the whole day for
smaller fleet sizes, but peaks again in the afternoon 5
for larger fleet sizes. This is because with more vehicles, more
demand is met at peak times. In Figure 4, 6
smaller fleets have no idle vehicles and are unable to meet the
demands at peak times for about 45% of 7
these passengers who are either going home or going to work.
8
As shown in Figure 6, average wait time reduces as fleet size
increases on average at [10, 8, 6, 5] 9
minutes for the fleet sizes respectively. For all fleet sizes,
high wait times are experienced in the morning 10
peak period when there are more rejections. The fare policy does
not have a significant effect on the wait 11
time, but the wait time for the $4 fare scenario is slightly
lower than the $2 fare scenario. 12
13
14
15
16
Figure 5: Passenger rides per time of day 17
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Kagho, Hensle, Balac, Freedman, Twumasi-Boakye, Broaddus,
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1
Figure 6: Wait time per time of day 2
3
Affordability 4
Figure 7 shows the distribution of DRT riders by income class at
each fare level with share of PT 5
users that switched to DRT from the baseline scenario. The PT
fare is set at $2 while DRT fares tested are 6
$2 and $4. Income classes of DRT users are similar to that of
public transport riders regardless of the fare 7
policy (INC1, INC2, and INC3 represent low, middle-low, and
high-middle high income earners 8
respectively). This shows that DRT fare is relatively affordable
for all income segments, which makes it 9
suitable for improving everyone’s mobility. 10
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16
1
2
Figure 7: Percentage of income levels of DRT users and PT users
(a: $2 fare for PT and 3
DRT, b: $2 for PT and $4 for DRT) 4
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17
System VKT impact 1
Private cars are dominant in Wayne County and contribute to
almost 80% of the mode share. An 2
interesting question is how the addition of vehicles that drive
almost all day, would impact total VKT. To 3
calculate this, all agents that used DRT are extracted from the
baseline scenario, their share of mode 4
replaced are compared, and the difference in VKT generated in
the transport network is computed. This is 5
summarized in Table 3 showing; the share of replaced mode in the
baseline scenario, the difference and 6
percentage change in VKT between replaced modes from baseline
scenario and DRT scenarios, and the 7
difference in system-wide VKT between baseline scenario and DRT
scenarios. 8
Looking at the share of replaced modes, the DRT trips come
mostly from car trips. Walk, public 9
transit and bike only contribute about 15% to the modes replaced
by DRT trips. However, these agents have 10
increased VKT in the transport system by switching to DRT. The
VKT change depending on fleet size has 11
the highest increase at 66% for 100 fleet and the lowest at 27%
for 1000 fleet. 12
Nevertheless, the percentage change of VKT on a system-wide
level is small. The highest across 13
the different scenario is about 0.07%. Further optimization on
fleet size, service parameters, as well as 14
limiting the service area, should minimize and possibly have a
positive impact on the system-wide VKT. 15
16
Table 3: DRT Impact on Mode Replacement and VKT 17
Fare_Fleet
Size
Share of Replaced Modes Difference in VKT for Replaced Modes Net
Change for
System-wide VKT
(%)
Bike
(%)
Car
(%)
PT
(%)
Walk
(%)
DRT
VKT
(km)
Baseline
VKT
(km)
VKT
difference
(km)
VKT
relative
change
(%)
$2_100 3.40 82.41 1.65 12.53 48309 27775 18280 65.81 0.031
$2_250 3.50 83.53 2.35 10.61 105541 70597 34944 49.50 0.060
$2_500 3.24 85.18 2.54 9.04 161701 121128 40573 33.50 0.069
$2_1000 3.10 86.06 2.57 8.26 188340 148660 39680 26.69 0.068
$4_100 2.97 83.76 2.25 11.03 43475 26128 17347 66.39 0.030
$4_250 2.65 86.13 2.38 8.85 79228 53863 25365 47.09 0.043
$4_500 2.49 86.98 2.53 8.00 92702 69002 23700 34.35 0.041
$4_1000 2.46 87.33 2.52 7.69 98218 76569 21649 28.27 0.037
Note: Overall VKT for baseline scenario is 58409642 km. This is
used in computing the difference in system
wide VKT (VKT difference per fleet/Overall VKT)
18
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CONCLUSION 1
This section summarizes some of the most important findings and
presents potential improvements of the 2
approach presented here to model a DRT service. 3
The results show that while the potential demand for DRT is high
in Wayne County, it is 4
challenging to serve the demand due to the large geographic size
and low population density of the modeled 5
area. While it is possible to serve a larger proportion of trips
with a relatively larger fleet, this results in 6
underutilization of the vehicles, as vehicles are unable to meet
wait time and detour time constraints for 7
picking up passengers. We find, based on the scenarios tested,
low potential for ridesharing in a DRT service 8
at the scale of Wayne County. Further analysis could test
smaller service areas with higher population 9
density which may have higher potential. 10
We found that the net increase in VKT due to DRT vehicles is
low, even with the largest fleet size 11
tested. We note that the current model does not consider two or
more people from the same household 12
traveling together, so the results would tend to under-estimate
vehicle occupancy as well as the VKT. We 13
also note that the simulation period ends by 4 PM, so certain
types of discretionary travel, such as dining 14
out, often undertaken by multiple persons from the same
household, are under-represented in the simulation. 15
While average wait times for DRT increase with respect to
demand, on average the wait times are 16
reasonable even at the highest demand period modeled. We also
find that pricing DRT similar to transit 17
results in greater mobility for lower income travelers, and that
pricing DRT at higher rates may substantially 18
reduce demand. 19
We identified several possible further improvements of the
methodology based on the limitations 20
of the study. The demand generated from the SEMCOG model is trip
based. Therefore, the activity-chains 21
of individuals are not preserved. This is a limiting factor as
certain constraints that tours impose are not 22
captured. A possible way to overcome this limitation is to
utilize an activity-based model, to generate full 23
daily plans (i.e. ActivitySim) or other approaches used to
generate mobility demand for MATSim (36). The 24
mode choice parameters for DRT are not estimated based on
empirical data. While in the context of the 25
Wayne County, it is reasonable to adapt the parameters for
public transport for DRT, it would be valuable 26
to conduct a stated preference mode-choice survey where DRT
service is explicitly captured. Information 27
on rejected agents in previous iterations is not used in the
following iterations when agents make mode-28
choice decision. It would be interesting to capture this measure
of reliability of the service in the mode-29
choice model. How individuals value reliability and how it
affects their choice set, is however an open 30
research question, and one worth investigating. 31
Based on the findings of the current study, for future
scenarios, we may want to test different service 32
parameters as well as rebalancing strategies. The DRT service in
this paper covers the whole study region. 33
Therefore, it is hard to meet pickup constraints, as the service
is spread thin within the region. Idle vehicles 34
are unable to service requests that are too far away from where
the vehicle is currently waiting. Using the 35
spatial and temporal information on the demand in the whole
region it would be possible to optimize the 36
service area in order to increase the share of pooled rides, and
potentially create a service that is able to 37
reduce the VKT in the region. In order to further optimize the
DRT service, and to make the final judgement 38
of its potentials in the region, additional sensitivity tests
are required. Those would include different cost 39
structures, fleet sizes, acceptable waiting and detour times.
Currently the DRT service does not anticipate 40
potential demand and does not perform preemptive relocations in
order to better serve the demand. To 41
overcome this limitation, different relocation policies could be
implemented in order to analyze their 42
potential improvements of the service. 43
In summary, there is potential for the DRT service to drive
demand and be efficiently utilized. 44
Presently the results show reasonable demand for the service,
low empty distance, and that the average 45
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19
VKT per vehicle lowers with increasing fleet sizes. However,
there is still the need to optimize the DRT 1
service parameter in order to maximize the efficiency of the
system and improve ride sharing. 2
3
ACKNOWLEDGEMENT 4
The authors would like to acknowledge Ford Motor Company for
funding this research, Justin Culp (RSG) 5
for contributions to network development, and Southeast Michigan
Council of Governments (particularly 6
Jilan Chen and Alex Bourgeau) for making the SEMCOG model and
data available to the project team. 7
8
AUTHOR CONTRIBUTION 9
The authors confirm contribution to the paper as follows: study
conception and design: all authors; data 10
preparation: Grace O. Kagho, David Hensle, Milos Balac, Joel
Freedman; analysis and interpretation of 11
results: all authors; draft manuscript preparation: all authors.
All authors reviewed the results and 12
approved the final version of the manuscript. 13
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Fishelson, Axhausen
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