Implementation of Transportation Management Strategies using
AVENUE for developing countries
Author Joy Bhattacharya1* , Ryota Horiguchi2
1. Transeva, 7Z/1F Cornfield Road, Kolkata, India, 2440-4125, joybhattacharya@sbcglobal.net
2. I-Transport Lab. Co., Ltd, JapanINTRODUCTION
Urban transportation trends are exacting significant costs in cities in both the developed andthe developing world. Growth of automobile traffic leading to serious congestion problems isthe bottleneck for all developing cities, especially obstructive for cities with high growthpotential. Highways have enhanced the mobility of every city although it is a challenge forurban highway system to meet the growing needs of developing cities and keep congestion
under control. The most direct approach to reduce congestion is through infrastructure
modifications by adding new lanes and bridges, which is by far the most expensive solution.
There are other solutions that focus on improving the efficiency of the existing infrastructure.
Operational modifications, better incident management, implementation of Intelligent
Transportation System, effective utilization of public transit system and other transportation
management strategies and few of the available strategies. Unfortunately, there exists noglobal solution or panacea and each situation is unique and requires customized solution.
This solution can be achieved through careful analysis of various feasible alternatives. Use of
traffic simulation is the only practical and cost-effective approach to compare
alternatives/measures to achieve the most appropriate traffic management solution. This
paper would introduce and illustrate how AVENUE, a real- time microscopic traffic
simulation model developed and widely used in Japan, enables planners to make more
informative and better traffic management decisions.
This paper would introduce and discuss the effectiveness of three traffic management
strategies in reducing congestion and provide real life case studies using AVENUE as thesimulation tool.
•••

Traffic Signal Control System
Transit Management System
Electronic Toll Collection

TRAFFIC MANAGEMENT AND SIMULATION“A toolbox for alleviating traffic congestion” [1] prepared by ITE, provides various strategiesof getting most out of the existing system for freeways, arterials and local street. Prior toimplementation of any traffic management strategy all feasible alternatives needs to beevaluated to select the desired alternative meeting the needs. This is the main advantage ofusing simulation tools that provide predictive capabilities. Once a model is developed andcalibrated for the base case, it can provide quick runs to analysis multiple future scenarios andcompare the measures of effectiveness. Another aspect of traffic simulation is its flexibility
to provide programmatic level (macro level) to operational (micro-level) evaluation. RecentImplementation of Transportation Management Strategies using AVENUE
advances in technology have made simulation models more and more user friendly andcapable to provide better visual representation.
Traffic simulation like any other analysis tool is heavily dependant on the quality of data
available. Sometimes collecting data for model calibration could make the use of the model
very expensive. Most of the micro-simulation models available commercia lly require
enormous data to calibrate drivers’ reaction to unique conditions. As stated in the“Guidelines for applying traffic simulation modeling software”, [2] the selection of theanalytical tool is the key part of any study. AVENUE is a microscopic simulation model that
requires limited data collection requirement due to its unique methodology.
AVENUE – A TRAFFIC SIMULATION MODELAVENUE (an Advanced & Visual Evaluator for road Networks in Urban arEas) is a
microscopic traffic simulation model developed in Japan [3]. The following featurescharacterize AVENUE:
1) Hybrid Block Density Method for its traffic flow model to enable reproduction ofover-saturated traffic conditions.
Flow2) Incorporated drivers' route choice
behaviour to meet the recent needs for
Capacity
the evaluation of dynamic route
Free Flow Speedguidance.
3) Drivers' lane choice model according totheir turning movements at theDensity
Critical Density Jam Density
intersection further ahead and traffic
regulations on road.
Density4) Designed and implemented in Object
Up Block
Down Block
Jam Dens.
Oriented Programming in order toTraffic Vol.
satisfy various users' needs.
Allowable Inflow
Critical Dens.
5) Graphical user interface with animatedAllowable Outflow
Flow
traffic images that provide easy
operation and persuasive presentations.
Block Length
Furthermore, the traffic model of AVENUE
was verified throughout “Standard Manual for the Verification of Traffic Simulation Models[4]” and validated with benchmark data set prepared and recommended for applying by theJapan Society of Traffic Engineers [5]. Throughout its operation of over 10 years, AVENUE
is now the most widely used traffic simulation model for the practical application in Japan [6].
At first, this paper illustrates some of the features of AVENUE, especially focusing on its
flow representation and highlighting its advantages in traffic modelling. Then this paper
presents the typical application case studies of AVENUE, which estimates the benefit of ITS.
Hybrid Block Density Method as the traffic flow modelHybrid Block Density Method (HBDM) is based on “block density method [7]”, whichcalculates traffic flow with continuum fluid approximation, and from the sake of simulation
utility, it is extended to move discrete vehicle images with various attribute values inaccordance with the flow.

2

Implementation of Transportation Management Strategies using AVENUEIn this method, each link is divided into several blocks of which length is equal to the distancethat a vehicle runs at the free flow speed of the link during a scanning interval, i.e. 1 second.
Normally the block length will be 10 to 20 meters. The in/out-flow and the density of eachblock are revised at every scanning interval based on the flow-conservation law, i.e. the flows
between two adjacent blocks are calculated by a function of the allowable out-flow of theupstream block and the allowable in- flow of the downstream block both of which aredetermined by the flow-density relationships of the link.
Since the fluid approximation of traffic has some difficulties in handling individual vehicle
information, this flow calculation was extended to move not only the continuum traffic
density but also the discrete vehicle images containing various attributes such as vehicle types,
origins and destinations, route choice criteria and turning movements.
Advantage of Hybrid Block Density Method to Car-following modelling approach
In the recent days, there is plenty of micro-simulation software available for commercial use.
Most of them calculate traffic flow with car- following (CF) model, which calculates theacceleration and the deceleration of each vehicle from the spacing and the relative speedbetween the vehicles. This modelling approach seems to be very natural for the humanbehaviour and affords the flexibility to the behaviour description.
However, CF modelling requires a lot of ‘personal’ parameters, e.g. desired speed, responsedelay, maximum acceleration, etc. It seems very difficult to adjust simulation traffic with realtraffic, because most of the personal parameters are not easily measurable. Many
practitioners using CF based simulation software, which is often made in European countriesand US, have suffered with its parameter calibration. Especially for Asian countries, whereexperience of simulation use is less, we cannot expect the accumulated knowledge on thoseparameter settings to match, and will face serious trouble to use CF models.
On the other hand, AVENUE requires only few parameters, capacity and free flow speed, todescribe traffic flow characteristics. These parameters can be easily measured at roadside.
Even though we do not know the exact values of the parameters, the accumulated knowledge
in the field of traffic engineering can tell us some appropriate values. Therefore, we believethat AVENUE’s approach for traffic modelling has a significant advantage, especially for its
application in Asian countries.
Case Studies of AVENUE for Traffic Management
Traffic Signal Control System
Asano et al. used AVENUE for virtual
experimentation to validate the effect of their newalgorithm for traffic signal control, CARREN [8],which directly measure the delay on the roads with
ITS sensors (e.g. image processing detector,
automatic ve hicle ident ifier, etc) and minimize thetotal delay. Another remarkable feature of CARREN
is adaptation. Most of the conventional signalcontrol methods would have some key parameters3

PC with CARREN
Emulated sensordata

TCP/IP

Signal parameters(cycle, split, offset)

Virtual field on AVENUEImplementation of Transportation Management Strategies using AVENUE

The performance of this algorithm was examinedusing AVENUE. The experiment was set up suchthat AVENUE provides the emulated sensor data
to external host computer via TCP/IP network. The
host computer calculates the optimal signal
parameters and AVENUE accepts the parameters
to control the traffic signal lights in the simulation.

Traffic signal lights actuatedby external host computer.
Emulated traffic sensors

70

without algorithm(major road)

60
delay (veh.*hr.)that greatly affect performance. However, after along-run, traffic situation may be different fromthose for which the traffic signal was calibrated,
and the parameters may not work well any more.
In CARREN, all key parameters are directly
measured from the site and updated at every cycle
to maintain the expected performance for a longtime.

50
40

with algorithm(minor road)with algorithm

8:4
0
8:5
0
9:0
0

7:4
0
7:5
0
8:0
0
8:1
0
8:2
08:3
0

7:1
0
7:2
0
7:3
0

6:50
7:0
030
The virtual experiment was conducted with the(major road)without algorithm
20(minor road)configuration in real road network. The study area10
was selected along Nagakute road in Nagoya-city,
0Japan. The network consisted of 11 intersections
over 5 km in length. On this network, wecoordinated a couple of case studies by varying the traffic demand, incident yielding lanes, etc.
Throughout the experiments, CARREN illustrated its capability to reduce total delay by 10 to30 percent as compared to conventional signal control algorithm in Japan.

National Police Agency of Japan coordinated the virtual experiments using AVENUE not
only for CARREN but also for other new algorithms proposed by other companies. The useof AVENUE was helpful for them to verify and validate the reliability of those algorithms inadvance prior to the real world implementation in field.
Transit Management System - Transit Mall in CBD
In order to improve the safety and comfort in CBDarea, Ministry of Land Infrastructure and Transportin Japan is now promoting ‘Transit Mall’, which
operates light rail transit running in ‘vehicle free’zone. At the planning stage, however, there isalways a controversial issue whether the traffic
condition in the surrounding areas would worsen ornot. Traffic simulation is the ideal tool available toestimate the impact of Transit Mall on traffic
congestion.

Transit Mall

AVENUE provides an extension module tosimulate exclusive lane use and exclusive signal control strategies for certain vehicle type,making it applicable for such impact analysis. The figure above illustrates the simulation
project conducted in Nagoya-city, which plans to construct a Transit Mall at the centre of thecity. In this case study, added to the impact analysis of diverting all vehicular traffic from4Implementation of Transportation Management Strategies using AVENUE
Transit Mall, the effect of reduc ing the impact with signal control and routing strategy was
also evaluated.
Electronic Toll Collection - Design of toll plaza with ETC exclusive lanes
In Japan, electric toll collection system (ETC) is
rapidly becoming popular due to the discountscheme of toll fare. The latest report shows thatapproximately 50% of traffic crossing tollbooths
use ETC in urban area. The road administrators
are anxious to enhance traffic safety and provide
smooth operation of the traffic in the vicinity oftoll plaza. AVEUNE was selected to evaluate thegeometric design of toll plaza as well as theoperation of ETC gates and separation strategies
from non-ETC vehicles.
For this case study, precise video survey was conducted to extract successive trajectories ofvehicles crossing the Narashino toll plaza in Chiba, Japan by using a set of video cameras [9]covering the entire area (500 meters) of the toll plaza. From the extracted trajectories of morethan 1000 vehicles, special lane change model in the toll plaza was identified andimplemented into AVENUE.
CAM -4

CAM-1

A conflict occurs between two adjacent vehicles

CAM-5
Trajectories of following ETC vehicles.

CAM -3

CAM-2
Leading ETC vehicle changes its lane here.

Figure 1. Successive video survey and extracted vehicle trajectories at Narashino Toll Plaza
As part of this study with AVENUE, a couple of scenarios were evaluated by fluctuating
traffic volume, changing the percentage of ETC vehicles, varying the positions of exclusive
ETC tollgates, etc. The evaluation uses the indices regarding the spatial distribution of speed
and interference in toll plaza. Figure 1 illustrates the video survey that was conducted atNarashino Tollbooth to extract the trajectories of vehicle. Upper portion of Figure 2, displays
the spatial distribution of ve hicle speed in the toll plaza and the lower portion shows thedistribution of interferences for two adjacent vehicles. For this illustration, the tollgate #0, #1,
and #-8 are operated as exclusive ETC lanes. Along the ETC exclusive lanes, we find theslower speed sections for around 80 meters upstream of the tollgate. There were highoccurrences of interferences observed for this section. Also, there were high interferences atthe sections about 220 meters upstream of the tollgate due to high frequency of lane changes.

5Implementation of Transportation Management Strategies using AVENUE

The result of this case study provides a helpful finding for the road administrators helping
them to take proactive decision. This study illustrates the following:-When the number of ETC vehicles becomes large, the capacity bottleneck will not
be apparent at the tollgate section but at around 70 to 80 meters upstream section.
To reduce the high interference at around 220 meters in the toll plaza, we have toprovide proper lane guidance signs further upstream.

toll gates

upper side
toll gates

upper side

Figure 2. Spatial speed (upper) and interference (lower) distributions in toll plaza.
CONCLUSIONSThis paper introduced a traffic simulation model based on the Hybrid Block Density method,summarizing the benefits of using a tool that would require minimum data collection andcalibration efforts to simulate traffic conditions in Asian countries. With three different casestudies for implementation of Traffic Management Strategies, this paper illustrates how asimulation model can effectively be used to provide valuable information to decision makers
and road administrators and help them undertake cost-effective solutions. Potential solutions
could be through the use of enhanced traffic signal control system or through the introduc tionof an innovative transit system along congested corridors. To reduce congestion at tollboothsections, adequate number of exclusive ETC lanes with proper signage could be considered tosmoothen its operation. Such effective use of traffic simulation can be extended todeveloping countries where better traffic management strategies would help alleviate trafficcongestion.

6

Implementation of Transportation Management Strategies using AVENUEREFERENCES[1] Michael D. Meyer, Ph.D., P.E..
enhancing mobility.

A toolbook for alleviating traffic congestion and[2] Federal Highway Administration Publication No. FHWA-HRT-04-040. Traffic Analysis
Toolbox Volume III: Guidelines for Applying Traffic Microsimulation Modeling
Software, pp. 15.
[3] Horiguchi, R. et al. (1994). A Development of a Traffic Simulator for Urban Road
Networks: AVENUE. In Proceedings of Vehicle Navigation & Information Systems,Yokohama, pp.245-250.
[4] Horiguchi, R. Kuwahara, M. Yoshii, T. (2001). A Manual of Verification Process forRoad Network Simulation Models - an Examination in Japan, In Proceedings of 7thWorld Congress on Intelligent Transportation Systems, Turin.
[5] Horiguchi, R. Kuwahara, M. (2003). Verification Process and its Application to Network
Traffic Simulation Models. Journal of Advanced Transportation, Vol. 36, No. 3, pp. 243264.
[6] http://www.i-transportlab.jp/products/avenue.
[7] Akahane, H. Oguchi, T. Oneyama, H. (2005). Saga of Traffic Simulation Models inJapan. In the book of International Symposium on Transport Simulation, Yokohama.
[8] Asano, M. Nakajima, N. Horiguchi, R. Oneyama, H. Kuwahara, M. (2003). Traffic
Signal Control Algorithm Based On Queuing Model Using ITS Sensing Technologies,
Proceedings of 10th World Congress on Intelligent Transport Systems, Madrid.
[9] Akahane, H. Hatakenaka, H. (2004). Successive Observations of Trajectories ofVehicles with Plural Video Cameras, International Journal of ITS Research, Vol.2, No.1.

7