Estimation of Travel Demand and Network Simulators
to Evaluate Traffic Management Schemes in Disaster
Shinji Tanaka, Masao Kuwahara, Toshio Yoshii, Ryota Horiguchi and Hirokazu Akahane*

Institute of Industrial Science, University of Tokyo, and*Department of Civil Engineering, Chiba Institute of Technology

1. Introduction
Traffic management in disaster has been a hot issue among traffic engineers since the Kobe
earthquake in January 1995. Because unusual traffic demand was generated onto the damaged
network, enormous traffic problems were encountered even in highways without any damage. In
addition, we faced quite a number of urgent traffic demand such as rescue, emergency medial care,
restoration of life lines, and so on. Through this experience, the importance of traffic management
during disaster is realistically noticed.
After the earthquake, many issues on road and traffic management have been vitally discussed. And,for the scenario earthquake in Tokyo, government officials made disaster mitigation plans which
include infrastructure planning, enforcement schemes, supply logistics, information provision, traffic
regulation, traffic demand management, etc. However, we may not quite understand that how muchand what kind of demand appears, what traffic condition arises under the mitigation policy, and if theplan is feasible.
To answer these questions, this research first proposes a method to estimate traffic demand mainly for
a 2-week period after a disaster and second develops traffic simulation models which can evaluate
management strategies beforehand.

2. Estimation of Traffic Demand in a Disaster2.1. Outline
In order to evaluate management strategies, we have to know the damage in the infrastructure supply
side as well as the traffic demand characterized by the disaster. Although many researchers andpractitioners have analyzed the level of damage expected due to a certain magnitude of an earthquake
in the supply side, the demand estimation has not been well studied yet. Since our travel behavior
would be quite different from the ordinary situation, this research proposes a method to estimate thedemand based upon the interview survey in Kobe/Osaka on travel behavior after the Kobe earthquake.
Then, the method is applied to the Tokyo prefecture to evaluate the Origin-Destination (OD) traffic
demand due to the scenario earthquake set up by the Tokyo Metropolitan Government6).
Roughly speaking, travel demand can be considered in two kinds: trips by ordinary people and trips by
emergency vehicles. We expect that the total amount of trips by emergency vehicles may be muchsmaller than trips by ordinary people. Also, the demand due to the emergency activities can beseparately estimated based on the degree of damage and its spetial distribution. Therefore, in thisstudy, we focus only on the trips by ordinary people during a 2-week period after an earthquake.

12.2. Interview Survey in Kobe/Osaka
Japan Safe Driving Center implemented the interview survey to drivers who visited the Driving
License Centers to renew their licenses5). The total number of 3,200 interviewees replied their travel
behaviors during a 2-week period after the Kobe earthquake. This survey is quite valuable tounderstand trip purposes, times, modes and distances immediately after the earthquake.
2.3. Trip Purposes Considered
In the interview survey, trips are classified into the following six purposes: (1) immediate evacuation(within 48 hours), (2) relief and rescue, (3) inquiry on safety, (4) evacuation for temporal stay, (5)acquisition of foods and clothes, and (6) checking up offices/schools. On the other hand, as
mentioned later, we intend to estimate the demand in the Tokyo prefecture under the scenario
earthquake occurred at 6 pm. in the evening on a weekday. In this occasion, lots of return-to-hometrips would be generated, although the interview survey did not include this trip purpose. As a whole,this research considers the five trip purposes below:(1) evacuation: both immediate evacuation within 48 hours and evacuation for temporal stay tosecure places to live mainly after 48 hours from the earthquake.
(2) return-to-home: trips from offices/schools to homes.
(3) relief/rescue and inquiry on safety: trips on relief/rescue as well as trips to confirm safety ofrelatives and acquaintances. This category also includes trips checking up theirhomes from evacuation camps.
(4) acquisition of foods and clothes: trips to obtain foods and clothes to live.
(5) checking up offices/schools: trips to check up offices and schools from homes and also from
evacuation camps.
2.4. Demand Estimation
For each of the above trip purposes, the demand estimation method is explained below.
(1) Evacuation
Trips for evacuation within 48 hours are assumed to arise in damaged districts and go to designatedopen spaces for evacuation. Daytime population in each district is multiplied by the percentage ofthis trip purpose and by the temporal distribution of the trip during a 2-week period. The
percentage of trip purposes and the temporal trip distribution are based upon the interview survey asshown in Fig. 2.1.

trips
700
600
500
400
300
200
100
0
1/17 1/19 1/21

Immediate Evacuation
Relief, Rescue
Safety Inquiry
Evacuation for Temporal Stay
Foods & Clothes
Offices/Schools

1/23 1/25 1/27 1/29

2

Fig. 2.1 Trip Shares by Purposes over the 2-week Period

Trips for evacuation for temporal stay are assumed to be generated after 48 hours and go toevacuation camps such as high schools, public halls, etc. or to relatives outside the damaged area.
Nighttime population is multiplied by the percentage of this trip purpose and by the temporal trip
distribution to obtain the generated trips in each district. Then, to estimate the trip distribution, thegenerated trips are allocated to each of the districts in proportion to capacities of evacuation camps
in a district. (Trips to relatives outside are neglected, since the locations of relatives' homes cannot
be known.)
Interview Survey

Daytime/ Nighttime Population% of the Trip TypeTrip Generation by Districts
Temporal Trip Distribution

Temporal Distribution of Generated
Trips

Government Statistics / Mitigation PlanOD Demand for Evacuation

Locations and Capacities of
Designated Open Spaces (- 48 hours)
Evacuation Camps (3 days -)Fig. 2.1. Estimation of Trips for Evacuation(2) Return-to-Home
This is a trip purpose from a place at the earthquake to a district of his/her home. First, daytime
population in each district is multiplied by the percentage of commuters to estimate the generated
trips by districts. Second, assuming that the temporal trip distribution is the same as one for
evacuation, the temporal distribution of the generated trips is obtained. Third, based upon the
person trip survey in a normal situation, the trip distribution is estimated.
Daytime Population

Government Statistics% of Commute Trips

Trip Generation by Districts

Interview Survey
Temporal Trip Distribution

Temporal Distribution of Generated Trips

Person Trip Survey
Trip Distribution of
Return-to-Home TripsOD Demand Return-to-Home

Fig. 2.2. Estimation of Return-to-Home Trips3

(3) Relief/Rescue and Inquiry on Safety
All these trips are assumed to be generated from residential districts just for simplicity, although inreality the trips would be also generated from designated open spaces for evacuation and evacuation
camps. And their destinations are either districts with casualties or hospitals. Similarly,
Nighttime population is multiplied by the percentage of the trip purpose and by the temporal trip
distribution to obtain generated trips. Then, the generated trips are allocated to each of the
districts in proportion to the number of hospitals and casualties.
Nighttime Population

Interview Survey% of the Trip TypeTrip Generation by Districts
Temporal Trip
Distribution
Temporal Distribution of Generated Trips

Government Statistics
Locations of Hospitals

Damage Assessment
OD Demand for Relief/ Rescue

Distribution of Casualties

and Inquiry of Safety

Fig. 2.3. A Flow-chart of Relief/ Rescue and Inquiry on Safety Trip Estimation(4) Acquisition of Foods and Clothes
Trips in this purpose are assumed to start from residential districts to distribution center for
emergency supply such as municipal offices/ schools and to private shops. Nighttime population ismultiplied by the percentage of the trip and by the temporal trip distribution to obtain the trip
generation over the 2-week period. Then, the generated trips are allocated to districts inproportion to the number of shops and the distribution centers.
Interview Survey

Nighttime Population% of the Trip TypeTrip Generation by Districts
Temporal Trip Distribution

Temporal Distribution of Generated

Government Statistics / Mitigation Plan
Locations and Capacities ofShops and Supply StationsOD Demand for Acquisitionof Foods and Clothes

Fig. 2.4. A Flow-chart for Estimation of Acquisition of Foods and Clothes4(5) Checking up Offices/ Schools
These trips are assumed to be generated from residential districts and absorbed in the working
places. Nighttime population is multiplied by the percentage of the trip purpose and by the
temporal trip distribution to obtain generated trips. Then, the trip generated is allocated to the
districts according to the person-trip survey in a normal situation.
Interview Survey

Daytime/ Nighttime Population% of the Trip TypeTrip Generation by Districts
Temporal Trip Distribution

Temporal Distribution of Generated Trips

Person Trip Survey
Distribution of Commute Trips
in a Normal SituationOD Demand for Checking Up
Offices / Schools

Fig. 2.5. A Flow-chart for Estimation of Checking Up Offices / Schools
The above procedure for each of five trip purposes estimates the origin-destination demand in person
trips by all modes. Person trips by vehicles are then obtained by multiplying the share of vehicle use
observed from the interview survey. In order to estimate vehicle trips, we further need the car
occupancy.
However, the occupancy cannot be known under the disaster condition, but it isexpected larger than in a normal condition.

3. Application to the Tokyo Prefecture
3.1. PremisesThis study follows the amount of damage estimated in the damage accessment for the scenario
earthquake by the Tokyo Metropolitan Government. An earthquake of magnitude 7.2 is supposed tohit the center of Tokyo at 6 pm. in the evening. The government estimates infrastructure damages,
the number of casualties, etc. by each of the districts. We also follow the disaster mitigation plan by
the government to figure out locations and capacities of evacuation camps and stocks of foods andclothes. Daytime and nighttime populations, distributions of schools, public halls and shops are
based upon the national statistics. And the data from the person trip survey are utilized especially for
estimation of commute and return-to-home trips.
3.2. Results
Figure 3.1 shows the share of person trips by purpose. On the first day, return-to-home trips share5

more than 50% because the scenario earthquake occurs at 6 pm. in the evening. On the other hand,on the third day, activities of relief/ rescue and inquiry on safety share about 25% and the almost same
share is found for acquisition of foods and clothes. For Fig. 3.1 (b), the number of person trips on thefirst day is about 1.4 trips/day, which is smaller than the normal dayly trips of about 2.5 trips/day.
And, on the third day, only 0.2 trips/day was observed, since the survey is just on activities related tothe earthquake. There must be quite a number of trips not closely related to the disaster on the third
day and thereafter.

Return to Home

1st day(a)Evacuation
Relief, Foods & Offices/
Rescue Clothes Schools

3rd day
0%

50%

100%

Foods &
Return to Home Clothes

1st day(b)

Relief, Offices/
Rescue Schools

Evacuation

3rd day
trips/person

0
Figure 3.1

0.5

1

1.5

The share of Person Trips by Purpose

Figures 3.2 shows total generated person trips by all modes in each of ten districts on the first day
after the scenario eqrthquake. Compared with the number of trips in the normal condition, more trips
are concentrated at the center of Tokyo.

62,000,000
person trips
Normal Condition
Disaster

10 km

Fig. 3.2 Total Generated Person Trips by All Modes on the First Day
Figure 3.3 shows person trips by vehicles on the first day. At the center of Tokyo, more than doubleof trips are generated by vehicles than those in a normal condition. Although the exact vehicle
occupancy cannot be known, the occupancy during disaster seems higher than about 1.2 - 1.3[persons/vehicle] in a normal condition. Therefore, the number of vehicles traveling immediately
after the scenario earthquake may not be larger as seen in the person trip base.

2,000,000
person trips
Normal Condition
Disaster

10 km

Figure 3.3 Person Trips by Vehicles on the First Day (Vehicle Users)

Figures 3.4 and 3.5 compare the trip distribution by vehicles on the first day with one in a normal
condition. As we see in the trip generation, widths of lines are generally wide during disaster.
Although change in the OD pattern is not very obvious, the 23 wards seem to be tightly connected by
the larger amount of trips.

7

200,000 person trips

10 km

Fig. 3.4 Trip Distribution in a Normal Condition on the First Day (Vehicle Users)

200,000 person trips

Fig. 3.5

10 km

Trip Distribution in the Disaster on the First Day (Vehicle Users)

4. Microscopic Simulation Models to Evaluate Traffic Management Schemes
In order to evaluate traffic management scheme in advance and to prepare reliable mitigation plan, it isnecessary to develop tools which estimates the traffic condition during disaster. For the recent
several years, we have been developing two types of microscopic traffic simulation models withdynamic route choices, AVENUE1, 2) and SOUND3). AVENUE may be applied to a surface street
network of which maximum sizes are approximately 5 x 5 square kilometers. On the network,
AVENUE has detailed traffic signals and lanes so as to evaluate the effects of signal control schemeand traffic regulations. On the other hand, SOUND deals with a larger network with a few
thousands of links and nodes. In SOUND, a link has no lane (always one lane), but traffic density isproperly controled based on the flow-density relationship even for a highway with two or more lanes
in one direction.
Our Simulation models requires the following input data:8

- network data (link connection data)- link capacity (including signal parameters)- time dependent Origin-Destination matrices- parameters in route choice model
In addition to the basic function of the models, traffic management scheme in disaster may require thefollowing special properties in the models:- closing roads for non-urgent vehicles,- traffic regulations such as one-way treatments, turning prohibitions,- on-street parking,- exclusive lanes for urgent vehicles or buses,- travel information broadcast,- signal control with priority direction,- routing around for special district, etc.
The evaluation of these scheme requires to manage different types of drivers and vehicles on the same
network. Microscopic simulation offers advantages to the conventional approaches based on flow
assignment technique because of the following reasons:
1) microscopic simulation can directly evaluate the service level of urgent vehicles or buses bycomputing their travel times,
2) flow assignment technique hardly incorporates lane management and signal control,3) microscopic simulation easily incorporates route choice behavior for multiple drivers' layers, etc.
Following sections demonstrate the applications of AVENUE and SOUND for the evaluation of
traffic management scheme in disaster.
4.1. Evaluation of Bus Lane Configurations using AVENUE
Through the survey at the Kobe earthquake in 1995, several remarkable bottleneck intersections werepointed out and some of them could be fixed with slight modifications on lane usage or signal control4).
Figure 4.1 illustrates Nishinomiya-honcho-crossing on Route 43. This intersection was the majorbottleneck for the traffic from Kobe to Osaka, and the length of the traffic jam heading from thisintersection sometimes reaches more than 15 kilometers.

9

Fig. 4.1. Nishinomiya-Honcho Intersection
At this intersection, the left lane on the link bound to Osaka was designated as a bus lane. The bus
lane reached to the downstream end of the link, nevertheless most buses turned left through the left
side turn pocket. Therefore, the downstream end of the left lane was not fully used by ordinary
vehicles, which reduces the capacity at this section.
Figure 4.2 illustrates one of the measures to fix this bottleneck with modification on the bus lane
designation4), i.e. to shorten the bus lane up to 60-70 meters upstream and let the both two lanes be
open for ordinary vehicles near the intersection. This idea may increase the throughput for the throughtraffic flow.

10Fig. 4.2. Modification on the Bus Lane Configuration

In order to evaluate how this modification would improve the bottleneck capacity, we applied
AVENUE for both "Before" and "After" the modification cases. As listed below, both cases have thesame saturation flow rate, the same signal cycle and split, and the same arrival traffic demand, buthave different designations of bus lanes.

S.F.R.
Cycle
Split
Demand"Before..."
1800
120
62.5
1150
180

"After..."1800[veh/hour of effective green/lane]120[sec.]
62.5[%] for major direction
1150[veh/hr] for straight180[veh/hr] for left turnFigure 4.3 shows display image of the simulation with AVENUE. The displayed scene is at the time of
11'56" passing from the beginning, however the upper link of "Before" case has been alreadycongested while the lower link of "After" case has not.
The result of both cases are compared in terms of cumulative throughput at the intersection and travel
times of buses. Figure 4.4 is the comparison of the cumulative number of vehicles passing theintersection in straight direction. Clearly the slope of "Before" case is less steep than "After" case, i.e.
the capacity at this section has been improved by the modification on the bus lane designation. Figure
4.5 shows the travel time of buses (from time a bus generated to time it passes through theintersection) for both cases. Big difference can not be found but even getting better in this comparison,
therefore we may conclude that shortening bus lane as in this modification will not affect the service
level of buses.

Fig. 4.3. Display Image of AVENUE

11

Cumulative number of vehicles (straight direction)
1600

Cumulative number of vehicles
[veh]

1400
1200
1000
800
600
400

Before

200

After

0
00:00

00:10

00:20

00:30

00:40

00:50

01:00

Time [min]Fig. 4.4. Comparison of Throughputs at the Intersection

L ink Travel Time of Buses
1.6
1.4
1.2
1
0.8
0.6
0.4

Before0.2

Afte r

0
0

5

10

15

20

Departure Time [min]Fig. 4.5. Comparison of Travel Times of Buses

4.2. An Application of SOUND

12

25

30

4.2.1. A Study Network
Fig. 4.6 shows the network used in our case study. It must be time consuming that we prepare
network data and link capacities. Therefore, we developed an software to prepare these data. The
software requires digital road map data, and digital road map are displayed and only thing we have to
do is selection of simulation areas on the display like Fig. 4.7. Once simulation areas are selected,
our software automatically prepares network connection data with link data such as distance,
width(number of lanes) etc. Link capacities are also required and it depends on signal parameters.
We have to preset signal parameters together with link capacities.

Fig. 4.6. A Study Network

Fig. 4.7. Display Image on the Software

4.2.2.

OD Demand

Time dependent OD matrices are also required in our simulator. Generally, it is prepared through aninvestigation and using an estimating method. And more, as it is difficult to estimate OD volumes
after an earthquake, we have to prepare several scenarios in which OD pattern is changed from usual
one, and the simulator should be implemented for every scenarios.
4.2.3. Route Choice Behavior

13

Depending on network traffic condition, each vehicles must change his route. In order to estimate
this kind of behavior, the simulator has to incorporate driver’s route choice. We employ the Dial’s
Logit assignment with parameterin which route choice probabilities are calculated fromEq.(4.1).

Pr ob( r ) =exp( −θ ⋅ Tr )∑ exp( −θ ⋅ Ti )

(4.1)

i

where
Prob(r) = route choice probability of route r,
Tr= travel time of route r,
θ= non-negative parameter4.2.4. Scenario
As an example, we suppose traffic condition just after an earthquake. In this scenario, it is assumed
that all signal systems are damaged and OD pattern does not changed caused by earthquake. We
express this effect of damage as reduction of saturation flow rate at each intersection approach. The
reduction rate at each approach is set between 20-80% using random sequence numbers. Fig.4.8
shows the display image of our tool. Displayed area size can be changeable. Fig.4.9 and 4.10
show traffic conditions at 30 min. after the earthquake in the average speed and in the vehicle densityof each link respectively. Using these images, we can understand what will be happen in a road
network. These results would be useful to propose a effective and feasible plan.

zooming

Fig. 4.8

Display Image of SOUND

14-10km/h
10-20km/h
20-30km/h
30km/h-Fig. 4.9. Average Speed at 30 min. After the Earthquake

150veh/km100-150veh/km
50-100veh/km-50veh/km

Fig. 4.10. Vehicle Density at 30 min. After the Earthquake
5. Summary and Future Research Needs
This research first proposes a methodology to estimate travel demand during a disaster and applies to
the Tokyo prefecture based upon the damage accessment for the scenario earthquake by the Tokyo
Metropolitan Government. Second, two kinds of traffic simulation models are developed to evaluate
traffic condition in advance when several management schemes such as information provision, traffic
regulations, travel demand management, etc. are implemented.
There would be still many rooms for future research especially on travel demand estimation. The
major research needs are summarized as follows:(1) The proposed method should be validated. The validation can be only possible by applying to the
Kobe earthquake.
(2) Travel behaviors investigated by the interview survey after the earthquake should be interpreted inrelation to the regional and network structures so as to hold the transferability of the estimation15

method.
(3) Reliable range of the estimated travel demand should be analyzed.
(4) Travel demand should be also estimated more time-dependently to represent phases after theearthquake.
(5) Traffic demand on emergency activities should be added to the demand of ordinary people.

References
1) R. Horiguchi, M. Kuwahara and I. Nishikawa : The Model Validation of Traffic Simulation System
for Urban Road Networks: 'AVENUE', Proceedings of the Second World Congress on Intelligent
Transport Systems '95, Yokohama, Vol. IV, pp.1977-1982, 1995
2) R. Horiguchi, M. Kuwahara, M. Katakura, H. Akahane and H. Ozaki : A Network Simulation
Model for Impact Studies of Traffic Management 'AVENUE Ver. 2', Proceedings of the Third
Annual World Congress on Intelligent Transport Systems, Orlando, CD-ROM, 1996
3) T. Yoshii, M. Kuwahara, H. Akahane and H. Morita : An On-Line Traffic Simulation to Predict
Near Future Travel Time on Urban Expressways, Proceedings of the Second Conference of theEastern Asia Society for Transportation Studies, Vol.2, No.4, pp.1237-1246 1997.10.
4) H. Akahane : Road Traffic Management in Urban Disasters, IATSS Review, Vol. 21, No. 2, pp.
31-39, 1995.
5) Investigation on Drivers’ Behavior in Disaster (I) - Human Travel Characteristics in Disaster :Japan Safe Driving Center, 1997.3.
6) Research on Damage Accessment for the Scenario Earthquake in Tokyo : Tokyo Metropolitan
Government, 1997.8.

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