Detecting Sudden Rises in Traffic Demand
Based on Route Search Request Logs
Kohei Ota*, Reimi Ishimura*, Naoki Nozu**NAVITIME JAPAN Co., Ltd.

1.

Introduction

People often specify a time in the future as a departure or arrival time when
they use online route search service (Figure 1). In this study, by using request logs of route search service, we attempted to detect sudden rises intraffic demand which have been difficult in a conventional method withstatistical or forecasting models.

Figure 1. Screen for entering route search requests (PC-NAVITIME).

2.

Data

In this study, we examined the possibility of detecting sudden rises in traffic
demand which happened from March 18 to April 14, 2014, using the request

logs between stations which were recorded from February 1 to March 17,2014 with several route search services provided by NAVITIME JAPAN Co.,
Ltd.

3.

Methods

It appears that the more route search requests were run in advance, theearlier forecasting a sudden rise in traffic demand becomes available.
First, we got the statistical increasing trend in the number of advancesearch requests (Figure 2). An advance search request is defined as a route
search request which were run prior to specified departure or arrival time.
Second, we forecasted the growth of the number of the future route searchrequests which would be run prior to the specified time.
Third, we detected sudden rises. In this study, we defined a sudden rise as acase which per-hour retrieval frequencies by specified station and timespecification method (i.e. a specification of either departure or arrival time)
become at twice the basic frequencies in an ordinary day and at least 50times.

Advance Search Rate

100%
80%

People run searches
for morning trips
much earier thanfor evening trips.

People run searches
by arrival time
much earlier thanby departure time.

60%
40%

[DPT] 6 a.m.
[ARR] 6 a.m.

20%

[DPT] 8 a.m.

[ARR] 8 p.m.

0%
4 daysbefore15 hours
before2 hours
before10 minbeforeReal TimeTime Difference between Request Time and Specified Time

Figure 2. Increasing trend in the number of advance search requests.

4.

Results

During the target period, total 12,268 sudden rises happened. We detected
267 0f them at 4 days before the specified time. The major part of them ishappened for concerts, sporting events and, because of the season, graduation and entrance ceremonies of schools and the government (Table 1).
Cause Event

Number Detected

Stations for Example

ConcertSeibu-Kyūjō-mae, Suidōbashi, Korakuen,
62 Saitama-Shintoshin, Shin-YokohamaSporting Event

Ukimafunado, Hasune and Keisei-Sakura
17 (for marathon), Urawa-misono

Other Event

Kokusai-tenjijō-seimon, Yokosuka-chūō (for12 U.S. Fleet Activities)

Holiday ResortKudanshita (for cherry-blossom viewing),
28 Takaosanguchi, Tokyo DisneySea Station

Government & BusinessDistrict

Nihon-ōdōri, Tochōmae, Kasumigaseki,
36 Nishi-shinjuku

School Event47

Time Schedule Revision

Wakōshi, Shinjuku-sanchome, Kita-sando,
15 Motomachi-Chūkagai

Airport2 Haneda Airport, Haneda Airport Terminal 1

Unknown

48

Total

267

Kudanshita, Hiyoshi, Chuo-Daigaku・Meisei-Daigaku,

Table 1. Sudden rises detected at 4 days before the specified time.

The following example is about a concert.
Figure 3 is a heat map, which shows top requested stations as origins ordestinations with the route search services between 4 and 5 p.m. on April 13,2014. Momoiro Clover Z, a Japanese idol group, held a concert on the day at
Seibu Dome. Seibu-Kyūjō-mae, the nearest station of there, was the 7th
most requested station at that time.
Figure 4 shows a time-based distribution of the number of route searchrequests of which the destination was Seibu-Kyūjō-mae and arrival date
was April 13, 2014. A number of search requests are concentrated before thecurtain time of the concert and the opening time of the merchandise booth.
At April 9, or 4 days before the concert, the retrieval frequency of the hour
before its curtain time had already become at 8 times the cumulated num-ber of the route search requests in an ordinary day. This indicated that wecan detect sudden rises in traffic demand by using request logs of routesearch service.

Between 4 and 5 p.m.
on April 13, 2013
Seibu-Kyūjō-mae

was the 7th mostrequested station.

CentralTokyo

The source data was downloaded from AIST's Landsat-8
Data Immediate Release Site, Japan（http://landsat8.geogrid.org/）.
Landsat 8 data courtesy of the U.S. Geological Survey.
Figure 3. Heat map of top requested stations. (Between 4 and 5 p.m. on April 13)

Seibu-Kyūjō-mae sta. on April 13, 2014
Real Time

Cumulated Number ofRoute Search Requests

2000

Before theOpening Timeof theMerchandise
Booth

1500

1000

Before theCurtain Time

10 min before
2 hours before
15 hours before
4 days before
Cumulated Numberin an Ordinary Day

8 times morerequested thanan ordinary dayat 4 days before

500

05678910 1112 13 14 15 16 17 18 19 20 21 22 23Specified Arrival Time of Route Search Requests
Figure 4. Time-based distribution chart of the number of route search requests.

5.

Discussion

We think that detecting sudden rises in traffic demand will improve public
transportation services and retail marketing approaches. Especially during
events such as 2020 Tokyo Olympics and Paralympics, for which forecasting traffic demand is quite difficult, our study would contribute to suppress
the traffic disruption and maximize its economic effects.

References
Ishimura R, Ota K & Tomii N (2013) Detecting Sudden Rise in Demand of Transportation
Needs in the Near Future Based on Immediate and Historic Data of Route Search Service(in Japanese). The 47th Research Meeting on Civil Engineering Planning, 2013