Development of the Nowcast Traffic
Simulation System using Floating Car Data
Hisatomo Hanabusa1, Masato Kobayashi1, Katsuaki Koide1, Ryota Horiguchi1,Takashi Oguchi2,
1 i-Transport Lab. Co., Ltd.
2 Institute of Industrial Science, the University of Tokyo

Abstract
The purpose of this study is to develop 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 floating car data provided in real time. Theoutput data is used for several traffic information services, including CO2 monitoring. In
this paper, we explain the concept of the Nowcast Traffic Simulation System and its
theoretical framework followed by an example of a demonstration experiment inKashiwa City in Japan.
Keywords: Traffic Simulation, Floating Car Data, Data Assimilation

1 Introduction
In recent years, real time traffic data collected by floating car data (FCD) can be used formany traffic information services. There are many advantages of real time traffic data.
For example, the accuracy of the travel time information for car navigation systems isimproved by using FCD. However, it must be noted that FCD is observed temporally andspatially as only a part of the traffic situation. In this case, one of the solutions is toestimate the traffic conditions between FCDs using some traffic models 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 theimportant research topics in the traffic engineering field. Some systems are developedand applied to real road networks for new traffic information services. It is said that thereproducibility and the accuracy of the estimated traffic situation are one of thechallenges for practical use. However, online traffic simulation can be a breakthrough forthe upgrading of traffic information services and road administration. In past studies,
Ishibashi et al. [Ish09] described the method of an online traffic simulation system andthe validation results, such as travel times on the Hanshin Expressway in Japan.
Munakata et al. [Mun09] described validation results on the Metropolitan Expressway

Development of the Nowcast Traffic Simulation System using Floating Car Data
using the online traffic simulation system developed by Shiraishi et al [Shi05].
DYNASMART-X[Dsx04], DynaMIT [Dym98] and Aimsun Online [Aim08] were also
developed and applied as online traffic simulation systems to predict traffic situations
using real time traffic data. These studies and examples are developed for application toroad networks in which traffic volumes and speeds are obtained by traffic sensors.
However, it is possible that there may be few sensors, or even no sensors, in regular roadnetworks compared to arterial roads and highways. In addition, the locations of origin
and destination of vehicles are difficult to determine. We can be fairly certain that thesolution in the case of insufficient sensors has not been studied enough. Althoughapplications 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 ofFCD (e.g. traffic volume cannot be estimated from FCD). Therefore, we developed aframework of an online traffic simulation system which can estimate the traffic
conditions of an entire road network using FCD in the case of insufficient traffic volumedata.

2 Concept
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 theimage of NTSS.

Figure 1: Concept of NTSS.

“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 be2

3 Methodology
applied to several kinds of traffic monitoring systems for city-wide road networks. The
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 theentire present traffic situation by a traffic simulation model using FCD. In this case, NTSS
calibrates the parameters of traffic simulation to fit the traffic situation given by the
traffic simulation model to the traffic situation from the observed data in real time. In
this study, the traffic simulation model SOUND is used and the parameters for thecalibration are below.
• OD traffic volume• Bottleneck capacity (saturation flow rate)

Figure 2 shows the image of the parameter calibration of NTSS. NTSS calibrates thesetwo types of parameters estimating the variation in the average traffic situation (thebase case).

Figure 2: Image of Parameter calibration of NTSS.

3 Methodology
In the parameter calibration process in NTSS, the methodology of Macroscopic
Fundamental Diagram (MFD) [Dag07] [Ger07] is applied. Figure 3 shows the concept ofthe evaluation using MFD. For NTSS, we assume that the aggregated traffic flow and theaggregated 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 the aggregated density (the QK plot)
between FCD and the simulation result are same, we can consider the traffic situation
between the real traffic and the traffic simulation. Thus NTSS searches the target
parameters to match both QK plots on MFD.
3

Development of the Nowcast Traffic Simulation System using Floating Car Data

3.1

Figure 3: Image of the evaluation using MFD.

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 andNTSS. Normally, we repeat steps to find the parameters considering the simulation
results and the objectives 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 the mathematical model to the conventional calibration.
The calculation time of traffic simulation is decreased substantially because the traffic
situations, including congestion, are modelled to the mathematical model. Therefore thereal-time estimation can be processed.

3.2

Figure 4: Calibration process of NTSS.

Macroscopic Fundamental Diagram

The Macroscopic Fundamental Diagram (MFD) can explain the macroscopic conditionsof road traffic by FCD, traffic sensors, and so on. Let 𝑙𝑗 denote the length of a link 𝑗, 𝑛𝑗τ
denote the traffic volume of link j at the time slot 𝜏 and 𝑇�𝑗τ denote the average travel43 Methodology
time of a link 𝑗 at a time slot 𝜏. An aggregated traffic flow 𝑄τ and an aggregated density
𝐾τ are defined in the following equation:𝑄τ = � 𝑙𝑗 𝑛𝑗τ
𝑗𝐾τ = � 𝑇�𝑗τ 𝑛𝑗τ
𝑗

(1)

(2)

Here 𝐿 is the total link length in a zone 𝑧. Using this function, we can put the flow andthe density 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 and the aggregated QK position of the simulation result on MFD. We assume thatthe 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 isminimized. Then the assessment function for the parameter search can be formulatedusing the aggregated QK plot 𝑢��⃑ 𝑧τ by the observed data and the aggregated QK plot 𝑢�⃑ 𝑧τ
by the simulation result at a time 𝜏 in a zone 𝑧 as follows:𝑇−1To minimize 𝐸 = ∑𝑧 ∑τ ��𝑢��⃑ 𝑧τ − 𝑢�⃑ 𝑧τ � Ω (𝑢��⃑ 𝑧τ − 𝑢�⃑ 𝑧τ )(7)

Where 𝐸 is the total distance of the QK plots between the observed data and thesimulation result on the MFD. The distance is calculated by the Mahalanobis’ generalizeddistance. Secondly, the traffic volume and the travel time for 𝑄τ and 𝐾τ are explained by
the traffic flow 𝑛𝑗τ and the average travel time 𝑇�𝑗τ of a link j at a time slot 𝜏. Let α denotethe mitigation rate of the saturation flow. The traffic volume 𝑛𝑗τ is calculated in the caseof a congested link at a bottleneck and otherwise as:𝑛𝑗τ = �α𝑞�𝑗τ𝑞𝑗τ

𝐶𝐶𝐶𝑔𝑔𝑔𝑔𝑔𝑔 𝑙𝑙𝑙𝑙 𝑎𝑎 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏
𝑂𝑂ℎ𝑒𝑒𝑒𝑒𝑒𝑒(8)Where 𝑞�𝑗τ is the traffic flow of the simulation result. On the other hand, 𝑞𝑗τ is the traffic

flow formulated as follows:

𝑟𝑟ℎ 𝑟𝑟ℎ𝑞𝑗τ = � � 𝑝̂𝑗τ
𝑄𝑄𝑟𝑟ℎ𝑟𝑟𝑗τ

≥ 0, �1 −𝑄 𝑟𝑟ℎ𝑄0𝑟𝑟ℎ�≤β(9)

𝑟𝑟ℎ𝑄 𝑟𝑟ℎ is the OD traffic volume of of a link j at 𝜏 for the od pair rs in a origin hour h. 𝑝̂𝑗τis5

Development of the Nowcast Traffic Simulation System using Floating Car Datathe link selection probability of of a link j at a time 𝜏. β is the constrained condition for
the adjustment of OD traffic volume. Finally, the average travel time 𝑇�𝑗τ for theaggregated density 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 averagetravel time. Let 𝑇jτ−1 denote the travel time of the first departure vehicle and 𝑇jτ
denote the travel time of the last departure vehicle.

Figure 5: Cumulative curve to calculate an average link travel time.

Therefore, 𝑇�𝑗τ is calculated as the following equation. 𝑇jτ−1 is the link travel time of thelast departure vehicle in the last time slot 𝜏.

1𝑇�𝑗τ = (𝑇jτ−1 + 𝑇jτ )2

In addition, 𝑇𝑗𝑗 is formulated by 𝑞�𝑗τ 𝑞𝑗τ , 𝑇𝑗𝑗−1 and τ.
𝑇𝑗𝑗 = 𝑇𝑗𝑗−1 + τ −α𝑞�𝑗τ
𝑞𝑗τ

(10)

(11)

𝑇𝑗𝑗 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 α𝑞�𝑗τ and 𝑞𝑗τ . If thelast simulation result (judged by the link travel speed) is not congested, 𝑇𝑗𝑗 is the sameas 𝑇𝑗𝑗−1 . On the other hand, if the last simulation result is congested, 𝑇𝑗𝑗 is changed by
the bottleneck capacity defined by α𝑞�𝑗τ and the traffic demand of 𝑞𝑗τ .

6

4 Validation

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 networkof Kashiwa City on the scale of 40 km square, consisting of 3566 nodes and 8171 links.
The traffic data in the operation test in November 2012 is used as the input data ofNTSS. Figure 7 shows the validation area.

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

The real-time data for NTSS is corrected by Automatic Vehicle Identification (AVI)
systems on the national highways of R16 and R6 around the Yobasuka junction. The
Yobatsuka junction is located in a suburban area of Tokyo. R16 and R6 are connected by
the service roads of R6 at this junction (R6 is elevated with grade separation design). Inaddition, the Yobatsuka junction is one of the strategic junctions for road traffic in thisarea. Therefore, a high concentration of traffic to the Yobatsuka junction causes chroniccongestion. There were 8 AVI systems around the junction. An AVI system can record
7Development of the Nowcast Traffic Simulation System using Floating Car Datavehicle plate numbers and the time vehicles passed. Therefore, the origin and thedestination, route and the travel time of each vehicle between these AVI’s can beidentified by matching the vehicle plate numbers. This AVI data can be regarded as FCD
in this study. Figure 8 is the parameter calibration results by NTSS in the evening peakhour on a weekday. We can see the OD traffic volumes using Route 16 (R16) areincreased. On the other hand, the OD 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 caseis 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 furtherconsideration, and we will continue to improve the accuracy of the reproducibility.

Figure 8: Variation of the traffic simulation parameters by NTSS.

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Figure 9: A validation example of travel time.

5 Application for CO2 Monitoring

5 Application for CO2 Monitoring
Figure 10 shows the experimental website for CO2 monitoring in Kashiwa City. An
operational test of NTSS has begun in the demonstration of the “Kashiwa Smart Project”[Kas13]. In the Kashiwa Smart Project, the present situation of CO2 emission in Kashiwa
City is monitored by NTSS in real time.

Figure 10: Experimental website of Kashiwa Smart Project.

The environmental information to reduce CO2 emissions from cars is delivered to thecitizens in Kashiwa City through the website and the mobile phone application. In thefuture, an increase of the amount of FCD is needed to improve the accuracy of the traffic
situation.

Acknowledgments
The Nowcast Traffic Simulation System was developed in the Energy-ITS Project
organized by the New Energy and Technology Development Organization (NEDO) inJapan. The Kashiwa Smart Project is a part of the development in the SCOPE (StrategicInformation and Communications R&D Promotion Programme) Project by the Ministryof Internal Affairs and Communications in Japan. The authors wish to thank Professor
Katsuki Ikeuchi from the University 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.

9Development of the Nowcast Traffic Simulation System using Floating Car Data

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KURAUCHI ，Takehiko DAITO: On-line Simulation Model for Traffic Control
System of Hanshin Expressway and Enhancement of its Validity Using
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[Mun09] 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.
[Shi05]

T. Shiraishi, R. Horiguchi, M. Kuwahara: A Development of Traffic Prediction
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[Aim08] http://www.aimsun.com/wp/?page_id=33[Dsx04]

http://www.its.uci.edu/~paramics/sim_models/dynasmart.html[Dag07]C. F. Daganzo, “Urban gridlock: macroscopic modeling and mitigation
approaches,” Transportation Research B 41, 49-62; “corrigendum”
Transportation Research B 41, 379, 2007.

[Dym98] Moshe Ben-Akiva ,Michel Bierlaire,Haris Koutsopoulos and Rabi Mishalani,“DynaMIT: a simulation-based system for traffic prediction”, DACCORD ShortTerm Forecasting Workshop February, 1998 Delft, The Netherlands[Ger07][Kas13]

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.
Experimental website for CO2 monitoring in Kashiwa city:

http://www.kashiwa-smart.jp/index.php?go=traffic-kashiwa

Corresponding author: Hisatomo Hanabusa, i-Transport Lab. Co., Ltd., Shin-Surugadai
DLDG 9F, 3-10, Kanda-Ogawamachi, Chiyoda-ku, Tokyo, 101-0052, Japan, phone: +81 35283-8527, e-mail: hanabusa@i-transportlab.jp

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