Development of the Nowcast Traffic Simulation System
for Road Traffic in Urban Areas

Hisatomo Hanabusa
Chief Researcher, i-Transport Lab. Co., Ltd.
Shin-Surugadai BLDG 9F, 3-10, Kanda-Ogawamachi, Chiyoda-ku, Tokyo, 101-0052, Japan
TEL:+81-3-5283-8527, FAX:+81-3-5283-8528, E-mail:hanabusa@i-transportlab.jp
Masato Kobayashi
Researcher, i-Transport Lab. Co., Ltd.
Shin-Surugadai BLDG 9F, 3-10, Kanda-Ogawamachi, Chiyoda-ku, Tokyo, 101-0052, Japan
TEL:+81-3-5283-8527, FAX:+81-3-5283-8528, E-mail:kobayashi@i-transportlab.jp
Katsuaki Koide
Researcher, i-Transport Lab. Co., Ltd.
Shin-Surugadai BLDG 9F, 3-10, Kanda-Ogawamachi, Chiyoda-ku, Tokyo, 101-0052, Japan
TEL:+81-3-5283-8527, FAX:+81-3-5283-8528, E-mail:koide@i-transportlab.jp
Ryota Horiguchi
CEO, i-Transport Lab. Co., Ltd.
Shin-Surugadai BLDG 9F, 3-10, Kanda-Ogawamachi, Chiyoda-ku, Tokyo, 101-0052, Japan
TEL:+81-3-5283-8527, FAX:+81-3-5283-8528, E-mail:rhoriguchi@i-transportlab.jp
Takashi Oguchi
Professor, Institute of Industrial Science, the University of Tokyo
Cw-504, Institute of Industrial Science, the University of Tokyo, 4-6-1 Komaba, Meguro-ku,Tokyo, 153-8505, Japan
TEL:+81-3-5452-6419, FAX: +81-3-5452-6420, E-mail: takog@iis.u-tokyo.ac.jp

ABSTRACTThis paper describes the development of an on-line traffic simulation system called the“Nowcast Traffic Simulation System” for road traffic in urban areas. The system canreproduce the traffic situation based on various types of traffic data provided in real time suchas floating car data and traffic sensor data. The output data is used for several traffic
information services, including CO2 monitoring. In this paper, we explain the concept of theNowcast Traffic Simulation System and its theoretical framework followed by an example ofa demonstration experiment in Kashiwa City in Japan.
Keywords: Traffic Simulation, Real Time, Floating Car Data, Macroscopic Fundamental
Diagram, Data Assimilation
11. INTRODUCTION
In recent years, real time traffic data collected by various traffic sensors and floating cars canbe used for many traffic information services. There are many advantages of real time trafficdata. For example, the accuracy of the travel time information for car navigation systems isimproved by using floating car data (FCD). However, it must be noted that the traffic data isobserved temporally and spatially as only a part of the traffic situation. In this case, one of thesolutions is to estimate the traffic conditions between sensors or FCDs using some trafficmodels or algorithms. An online traffic simulation system should be provided as an example
of a framework to estimate all traffic conditions in an area. ‘Online traffic simulation systems’is one of the important research topics in the traffic engineering field. Some systems aredeveloped and applied to real road networks for new traffic information services. It is said
that the reproducibility and the accuracy of the estimated traffic situation are one of thechallenges for practical use. However, online traffic simulation can be a breakthrough for theupgrading of traffic information services and road administration. In past studies, Ishibashi etal. [1] described the method of an online traffic simulation system and the validation results,
such as travel times on the Hanshin Expressway in Japan. Munakata et al. [2] describedvalidation results on the Metropolitan Expressway using the online traffic simulation systemdeveloped by Shiraishi et al [3]. DYNASMART-X[4] and DynaMIT [5] were also developed
and applied as online traffic simulation systems to predict traffic situations using real timetraffic data. The Local Madrid Road Authorities & Telvent Consultancy introduced a traffic
prediction system, Aimsun Online [6], to the traffic control system. These studies andexamples are developed for application to road networks in which traffic volumes and speeds
are obtained by traffic sensors. However, it is possible that there may be few sensors, or evenno sensors, in regular road networks compared to arterial roads and highways. In addition, thelocations of origin and destination of vehicles are difficult to determine. We can be fairly
certain that the solution in the case of insufficient sensors has not been studied enough.
Although applications of FCD, such as travel time prediction, have been the object of study inrecent years, no studies have ever tried application according to the characteristics of FCD(e.g. traffic volume cannot be estimated from FCD). Therefore, we developed a framework ofan online traffic simulation system which can estimate the traffic conditions of an entire roadnetwork using FCD in the case of insufficient traffic volume data.
2. CONCEPT OF THE NOWCAST TRAFFIC SIMULATION SYSTEM
The purpose of this study is to develop the Nowcast Traffic Simulation System (NTSS) toestimate the road traffic conditions in urban areas in real time. Figure 1 shows the image ofNTSS. “Nowcasting” is the meaning to predict some sort of present situation such as weather.
Thus NTSS forecasts the present traffic situation using real time traffic data. NTSS can beapplied to several kinds of traffic monitoring systems for city-wide road networks. The2purpose of the monitoring is to measure not only traffic flow and travel time but also
environmental impacts such as CO2 emission and traffic noise. NTSS complements the entire
present traffic situation by a traffic simulation model using real time traffic data (FCD andtraffic sensors). In this case, NTSS calibrates the parameters of traffic simulation to fit thetraffic situation given by the traffic simulation model to the traffic situation from the observed
data in real time. Figure 2 shows the image of the parameter calibration of NTSS. NTSScalibrates these two types of parameters estimating the variation in the average traffic
situation (the base case). In this study, the traffic simulation model SOUND is used and theparameters for the calibration are below.


OD traffic volume
Bottleneck capacity (saturation flow rate)

Figure 1: Concept of NTSS

Figure 2: Image of Parameter calibration of NTSS3

3. METHODOLOGY
In the parameter calibration process in NTSS, the methodology of Macroscopic Fundamental
Diagram (MFD) [7] [8] is applied. Figure 3shows the concept of the evaluation using MFD.
For NTSS, we assume that the aggregated traffic flow and the aggregated density on MFD are
calculated by FCD. We divide the target road network area into some zones for MFD. Here,
we assume that the relationship between the MFD aggregated by FCD and the MFD
aggregated by the simulation result is similar. The extended MFD of FCD can be compared to
the MFD of the simulation result. If the two positions of the aggregated traffic flow and theaggregated density (the QK plot) between FCD and the simulation result are same, we canconsider the traffic situation between the real traffic and the traffic simulation. Thus NTSSsearches the target parameters to match both QK plots on MFD.

Figure 3: Concept of the evaluation using MFD

3.1 Strategy for the Parameter Calibration
To search for the most suitable parameters, we introduce a mathematical model. Figure 4
shows the comparison of the calibration process between a conventional method and NTSS.
Normally, we repeat steps to find the parameters considering the simulation results and theobjectives such as traffic volume and travel time. Conventional calibration is needed to run
traffic simulation to find the next parameters every time. On the other hand, NTSS adds themathematical model to the conventional calibration. The calculation time of traffic simulation
is decreased substantially because the traffic situations, including congestion, are modeled tothe mathematical model. Therefore the real-time estimation can be processed.

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Figure 4: Calibration process of NTSS

3.2 Macroscopic Fundamental Diagram
The Macroscopic Fundamental Diagram (MFD) can be explained the macroscopic conditionsof road traffic by FCD, traffic sensors, and so on. Let
denote the length of a link ,denote the traffic volume of link j at the time slot

denote the average travel time

andof a link at a time slot . An aggregated traffic flow
are defined in the following equation:

and an aggregated density

(1)
(2)

Here
is the total link length in a zone . Using this function, we can put the flow and thedensity by observed data and the simulation result on the MFD.
3.3 Evaluation Formula for Parameter Calibration
First, let us explain about the assessment function for parameter calibration. To fit theparameters, we use the distance between the aggregated QK position of the observed data andthe aggregated QK position of the simulation result on MFD. We assume that the simulation
parameters (OD traffic volume and link capacity) fit when the distance of the aggregated QK
positions between the observed data and the simulation result is minimized. Then theassessment function for the parameter search can be formulated using the aggregated QK plotby the observed data and the aggregated QK plot
in a zone as follows:
To minimize

by the simulation result at a time∑ ∑5(7)Whereis the total distance of the QK plots between the observed data and the simulation
result on the MFD. The distance is calculated by the Mahalanobis’ generalized distance.
andare explained by the traffic
Secondly, the traffic volume and the travel time forflow
and the average travel timeof a link j at a time slot . Let α denote themitigation rate of the saturation flow. The traffic volume
is calculated in the case of acongested link at a bottleneck and otherwise as:
αWhere(8)is the traffic flow of the simulation result. On the other hand,is the traffic

flow formulated as follows:
̂(9)
0, 1

β

for the od pair rs in a origin hour h. ̂

is the OD traffic volume of of a link j at

the link selection probability of of a link j at a time .

isβ is the constrained condition for theadjustment of OD traffic volume. Finally, the average travel time

for the aggregateddensity is calculated by the link travel times using the cumulative vehicle count curves. Figure
5 shows the definition of the link travel time and the average travel time. Letthe travel time of the first departure vehicle anddenote the travel time of the lastdeparture vehicle.

Figure 5: Cumulative curve to calculate an average link travel time6

denoteTherefore,

is calculated as the following equation.

is the link travel time of thelast departure vehicle in the last time slot .
12In addition,is formulated by(10)and τ.

,

τ

α(11)

can be changed according to the traffic situation. Figure 6 shows the image of thevariation of the cumulative vehicle count curves by the traffic flow α
simulation result (judged by the link travel speed) is not congested,
On the other hand, if the last simulation result is congested,
capacity defined by α

and the traffic demand ofand. If the lastis the same as.

is changed by the bottleneck.

Figure 6: Variation of cumulative vehicle count curves

4. VALIDATION
For the validation of NTSS, we developed an on-line system of NTSS for a road network ofKashiwa City on the scale of 40 km square, consisting of 3566 nodes and 8171 links. Thetraffic data in the operation test in November 2012 is used as the input data of NTSS. Figure 7
shows the validation area.

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Figure 7: Validation area (Kashiwa City, Chiba, Japan)

Figure 8 is the parameter calibration result by NTSS in the evening peak hour on a weekday.
We can see the OD traffic volumes using Route 16 (R16) are increased. On the other hand, theOD traffic volumes using Route 6 (R6) are decreased. The capacity parameters are also
calibrated based by NTSS to fit the present traffic situations. Figure 9 shows the validation
results of the travel time of R16. The base case is the average travel time as an initial value
before the calibration by NTSS. The travel time of a section of R16 is estimated more
precisely. The results need further consideration, and we will continue to improve theaccuracy of the reproducibility.

Figure 8: Variation of the traffic simulation parameters by NTSS8

Figure 9: A validation example of travel time

5. APPLICATION
An operational test of NTSS has begun in the demonstration of the “Kashiwa Smart Project”[9] for the ITS World Congress Tokyo 2013. Figure 10 shows the experimental website forCO2 monitoring in Kashiwa City. In the Kashiwa Smart Project, the present situation of CO2emission in Kashiwa City is monitored by NTSS in real time. The environmental information
to reduce CO2 emissions from cars is delivered to the citizens in Kashiwa-shi through thewebsite and the mobile phone application. For example, a CO2 visualization system by virtual
reality techniques and a Navigation and Eco-driving support system were developed. In thefuture, an increase of the amount of FCD is needed to improve the accuracy of the trafficsituation.

Figure 10: Experimental website for CO2 monitoring in Kashiwa City96. ACKNOWLEDGMENT
The Nowcast Traffic Simulation System was developed in the Energy-ITS Project organizedby the New Energy and Technology Development Organization (NEDO) in Japan. The
Kashiwa Smart Project is a part of the development in the SCOPE (Strategic Information andCommunications R&D Promotion Programme) Project by the Ministry of Internal Affairs andCommunications in Japan. The authors wish to thank Professor Katsuki Ikeuchi from theUniversity of Tokyo, the members of Kashiwa Smart Project, the Chiba National Highway
Work Office of Ministry of Land, Infrastructure, Transport and Tourism and Chiba Prefectural
Police Headquarters for their support to promote this research and development.
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Expressway and Enhancement of its Validity Using Dynamic Observations, 2ndInternational Symposium on Freeway and Tollway Operations, 2009.
[2]K. Munakata, Y. Tamura, H. Warita, T. Shiraishi: A Case Study about the Traffic Prediction
under Accidents Using Dynamic Traffic Simulation on Tokyo Metropolitan Expressway,
Proceedings of 16th World Congress on Intelligent Transport Systems, Stockholm, 2009.09.
[3]T. Shiraishi, R. Horiguchi, M. Kuwahara: A Development of Traffic Prediction System
Based on Real-time Simulation, Proceedigs of 12th World Congress on Intelligent Transport
Systems,CD-ROM, 2005.11.
[4]http://www.aimsun.com/wp/?page_id=33[5]http://www.its.uci.edu/~paramics/sim_models/dynasmart.html[6]Moshe Ben-Akiva ,Michel Bierlaire,Haris Koutsopoulos and Rabi Mishalani, “DynaMIT: asimulation-based system for traffic prediction”, DACCORD Short Term Forecasting
Workshop February, 1998 Delft, The Netherlands[7]C. F. Daganzo, “Urban gridlock: macroscopic modeling and mitigation approaches,”
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[8]N. Geroliminis and C. F. Daganzo, “Existence of urban-scale macroscopic fundamental
diagrams: some experimental findings,” Working paper, Volvo Center of Excellence on Future
Urban Transport, Univ. of California, Berkeley, 2007.
[9]Experimental website for CO2 monitoring system in Kashiwa city (the SCOPE Project),
http://www.kashiwa-smart.jp/index.php?go=traffic-kashiwa

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