4. aimsun..simulacion via congestionada.pdf

6th EURO Working Group on Transportation, Gteborg, Sweden 13/09/02 15:39 By: John Hughes, Transit NZ, Auckland File: Euro4.doc

ABSTRACT

Title: AIMSUN2 Simulation of a Congested Auckland Freeway

Author: John T Hughes, BE (Civil), MIPENZ Regional Transportation Engineer Transit New Zealand, P. O. Box 1459 Auckland, New Zealand

Contact Details: Tel: 64-9-377 7092

Fax: 64-9-307 6843 Email: [email protected]

Introduction and Objectives The objective of this paper is to present an outline of the model building process and selected findings from a traffic simulation model of a congested 9.7 km section of an urban freeway in Auckland, New Zealand. Field data was collected to provide a comprehensive set of New Zealand traffic characteristics for the freeway micro-simulation model. The data set constitutes a detailed "snapshot" of traffic conditions over one week (23 to 29 September 1997), with additional detail on particular days. The data, which include traffic speeds, volumes, headways, accelerations and lane change counts, are being used to build an AIMSUN2 traffic simulation model. This is a new tool that can provide input into transport investment decisions in Auckland.

Simulation Model AIMSUN2 (Advanced Interactive Microscopic Simulator for Urban and Non-Urban Network) is a microscopic, stochastic model for simulating traffic on road networks. It is part of the GETRAM (Generic Environment for Traffic Analysis and Modelling) software suite developed at the Universitat Politcnica de Catalunya in Barcelona, Spain GETRAM (Barcelona and Ferrer 1997, Monteroet al 1998) consists of a user-friendly graphical interface, a traffic network graphical editor (TEDI, Traffic Editor) supporting any kind of road type or network geometry, a network database and a module for storing and presenting results. It includes an animated simulation display, which shows vehicles moving through the network. The model can simulate a range of traffic management features including incident detection and surveillance systems, variable message signs and wide area traffic control strategies. Simulating predictive control and guidance strategies are also potentially feasible. AIMSUN2 has a wide variety of possible applications in traffic management of Aucklands congested arterial road network. In particular it may be an important enabling technology to serve as a testbed for an Advanced Traffic Management System (ATMS) being developed in Auckland. Transit New Zealand (Transit), the national State Highway authority, is implementing the ATMS, the first portions of which are scheduled to be operational by mid 1999.


The traffic data collected on Aucklands Southern Motorway are being used to calibrate and validate the model to determine the accuracy with which it can represent real traffic flows. If it is shown that the model is accurate over a section of motorway by comparison with extensive measured data then confidence can be had in simulations of other motorway sections and features where less comprehensive data is available. Study Area and Scope Auckland is located towards the north of New Zealands North Island. With a population of some 1.1 million people it is the countrys largest urban centre and is growing at 2.5% per year. The study area (Figure 1) is located south of the central business district (CBD) and passes through the regions core industrial areas of Penrose and Mt Wellington. The study section is a 9.7 km length of the Southern Motorway extending from Panama Road (just south of Mt Wellington Highway) in the south to the Khyber Pass in the north. The motorway section crosse relatively flat terrain with an isolated maximum grade of 4.0% and the balance at 3.0% or less. In 1997 it carried bi-directional Average Annual Daily Traffic (ADDT) volumes ranging from 109,000 vehicles per day (vpd).

Figure 1 Study Area

Spaghetti Junction (CBD)

Ellerslie

Gillies Avenue Market

Road Greenlane

Panama Road

Mt Wellington Highway

Tamaki River

Khyber Pass

Main Highway

Penrose Road

Southern Motorway (SH1)

Southern Motorway (SH1)

0 1 2 km


To obtain information that was comprehensive, yet at a sufficiently high level of detail, many different types of data were collected simultaneously at varying intervals along the motorway. Automatic count data was collected over a full 7-day week. Resource-intensive data collection methods, including video taping, aerial photography and laser-gun speed profiles, were measured on a single day, mostly within that week. Most of the data are being used to calibrate the AIMSUN2 model. Some data not used in the calibration are being used as benchmarks to which simulation model output is being compared in order to validate the model. The work reported in this paper is a preliminary investigation into the ability of the model to reproduce traffic flows in the northbound direction of this motorway corridor. The general approach was to define the motorway links, apply field-measured traffic flows at the network boundaries and then calibrate the model by fine-tuning sensitive parameters to seek agreement with measured data at intermediate points within the corridor. These steps are repeated to seek a good agreement with the field results. Model Requirements In common with many simulation models (Wang and Cassidy 1995, Hua Heng 1989, Quadstone 1996) AIMSUN2 requires input information defining the road network geometry, traffic stream conditions and driver and vehicle characteristics. Geometric Information For this project the motorway geometric layout was obtained as a CAD (dxf) file showing curb lines and edges of the road pavement. The map was geometrically accurate, having been produced by aerial photogrammetry. However it was not particularly detailed and omitted key features, such as lane lines and other pavement markings. Additional information on the widths and number of lanes and the lane configuration at ramp locations was obtained from a variety of sources. These included historic construction drawings dating back to the 1960's, a series of 1:1,000 scale ortho-corrected black and white photographic prints from 1994 and uncorrected colour photography flown during the traffic surveys on 26 September 1997. The dxf file was imported into TEDI and the roadway links created by mouse dragging and clicking the appropriate section drawing tools over the map background. While this is the usual method of creating a new AIMSUN2 model an alternative is available. That is to import an EMME/2 network model, with it's centriod connector structure, directly into GETRAM using the optional module available for this purpose (Montero et al 1998). Table 1 shows the basic road section parameters adopted in this model from field studies of the southern motorway (Hughes 1998).


Traffic Flow Information Traffic flow information includes trip demands across the network, fleet composition (cars, trucks, buses etc) and traffic control mechanisms such as traffic signals and intersection priority signage. As this model involves only the motorway through lanes and ramps, and not the adjacent arterial network, it has no at-grade intersections, priority signs or signals. During one week in September 1997, additional traffic data was obtained over and above that normally collected in Transits ongoing traffic monitoring program. Permanent count stations with inductive loop detectors are located on the motorway through lanes at each end of, and at 2 locations within, the study area. These record time-interval count and speed data plus vehicle headways and length classifications. For this study data was obtained by using a video classification system for 2 hours each, at four additional locations in the morning and afternoon peaks on Friday 26 September. Unclassified vehicle counts were collected on all 17 ramps entering and leaving the motorway study section (Hughes 1998). In the past micro-simulation models have tended to require defined traffic flows on each entry link to the network and specified turning percentages (by vehicle type) at each intersection or off-ramp. With this approach the vehicles entering the network have no "knowledge" of their intended route or destination. The more recent trend in microscopic simulation is for traffic inputs to be defined as time-sliced origin-destination matrices. This allows greater flexibility in modelling traffic scenarios and problems involving route assignment can be investigated at the microscopic level. AIMSUN2 allows the use of both methods of traffic data input. Route choice is not an issue in this Auckland Motorway model, and the generation of time-sliced matrices can also be difficult and time consuming. However the matrix method has been used in this study because the alternative, requiring the calculation of turning percentages from count data by vehicle type and time interval, would also not have been straight forward. The latter method could give incorrect flows when simulating transient phenomena such as lane blockages. Errors could occur, for example, if the motorway through lanes were blocked up stream of a bottle neck that is normally caused by a heavy on-ramp merge. The through lane blockage would allow higher than normal flows to enter the motorway at the ramp. Application of the normal (un-blocked) percentage for vehicles leaving the motorway at the next down stream off ramp would probably be erroneous if most of the through traffic had actually been blocked from reaching that point by an accident. Trip matrices were obtained from a 1992 origin-destination postcard survey for each of three different vehicle types during the 7.00am too 9.00am morning commuter peak . These were manually factored in a spreadsheet to approximately match measured traffic flows entering and leaving the motorway study section on 26 September 1997. The end result was a total of 48 matrices (3 vehicle types per quarter hour from 6am to 10am) which were applied to the study network.


Driver and Vehicle Information Vehicles and drivers have a range of characteristics which effect the way they travel through a road network. These include mechanical attributes of the vehicle (eg: size, performance levels) and aspects of driver behaviour (eg: desired speed, acceleration and gap preferences). In GETRAM this type of information is input as parameters pertaining to vehicle types, any number of which can be defined by the user. The data which may be entered for each vehicle type are shown in Figure 2. These include desired speed, acceleration, normal and emergency deceleration, maximum yield time and minimum vehicle spacing when stopped in a queue. The queuing up and queue leaving speeds control whether or not a vehicle will enter an intersection that contains vehicles which are "queued", as defined by these parameters. In a traffic stream these data may vary stochastically between vehicles. For each data item (desired speed, acceleration etc) the values attributed to individual vehicles are considered normally distributed and the user may define the distribution parameters (mean, standard deviation, minimum and maximum values).The three vehicle types currently being used for this study are shown in Table 2. Table 2 Vehicle Type Classifications

Vehicle Type Classification Length Class (metres) Weight Class (tonnes) Passenger Car (CAR) < 5.5m < 3.5t Light Commercial (LCV) 5.5m to 11.0m < 3.5t Heavy Commercial (HCV) > 11.0 > 3.5t AIMSUN2 does not use vehicle weight as a model parameter. However, pending more specific data, the weight classes have been assumed to correspond to the length classes shown in the table. This enabled trip matrices from an earlier postcard Origin Destination survey to be used in the study. The following data was obtained from individual vehicles recorded at three of the motorway ATMS sites on 26 September 1997. Table 3. Vehicle Length (m)

Mean Min Max Std. Dev. Sample Size Cars 4.39 ? 5.5 0.43 82,392 LCVs 7.73 7.5 11.5 1.49 4,535 HCVs 16.64 11.5 ? 3.55 2,168 Total: 89,095


Table 4. Vehicle Desired Speeds

Free Speed (km / hr) Mean Min Max Std. Dev. Sample Size

Cars 96 ? ? 15.3 3217 LCVs 88 ? ? 12.9 306 HCVs 90 ? ? 14.9 173 Total: 3,696 The free speed data were derived from a sample of vehicles travelling at a time gap of 10 seconds or more behind the vehicle ahead. A small number of data records were discarded as outliers. These were erroneous length records (ie. < ?? or > ??m) and suspect free-speed records (ie. < ?? or > ?? km/h). Maximum Vehicle Acceleration As each simulated vehicle enters the modelled road network it is assigned three speed-change parameters. These are its maximum acceleration rate and its normal and maximum (or emergency braking) deceleration rates. Although it is difficult to measure speed change parameters which are characteristic of whole fleet and driver populations, some information was gleaned from three local sources. These were a study of traffic decelerating on a motorway off ramp (Bennett 1993), instrumented vehicle trials for development of a fleet emissions control strategy (Ministry of Transport 1997), and some laser gun speed change measurements by the author. Bennett recorded average deceleration rates from 2000 vehicles on Aucklands Grafton off ramp of between from 0.46 m/s and 2.34 m/s, for various approach and final speeds. He also cites a study on urban streets in Palmerston North, New Zealand (ATS 1990) which reported the following maximum rates for vehicles travelling at less than 70km/hr . Table 5. ATS Study

Maximum Rate m/s Vehicle Acceleration Deceleration Passenger Car 1.08 -1.72 Heavy Trucks 0.40 -1.19 The New Zealand Ministry of Transport (MOT) conducted an extensive series of trials using 23 instrumented vehicles following qualitatively defined drive cycles on Auckland area roads (Ministry of Transport 1997). The vehicles were all cars of various ages and conditions with engine capacities ranging from 1.3 to 4.1 litres. The aggregated results included the following:


Table 6. NZ MOT Study

Road Type Traffic Type Average Speed (km/hr)

Max Speed (km/hr)

Max Acceleration

(m/s)

Acceleration Standard

Deviation (m/s) Suburban Interrupted

Aggressive 31.8 74.9 4.0 1.17

Suburban Interrupted 23.4 58.0 2.8 0.79 Urban Interrupted 15.4 54.9 3.2 0.88 Urban Congested 7.7 49.3 2.8 0.65 Motorway Congested 32.9 74.1 1.8 0.66 The author measured vehicle speeds, and hence accelerations at one location on Aucklands Southern Motorway and at signalised urban arterial intersection on Quay Street. Speed changes were measured over successive pairs of observations of each vehicle. Scatter plots of the resulting data are shown in Figure 2 and summarised in Table ? below. Figure 2 Laser-measured Speed Changes. Each of the three sources of field data give an indication of the range of speed-change values appropriate for the model. However none of them closely corresponds to the desired statistics, namely the probability distributions for the maximum accelerations and decelerations experienced by the population of vehicles and drivers. Figure 3 shows the vehicle parameters adopted from the model runs reported in this paper.

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Figure 3 Vehicle Parameters Used in this Model Motorway Model A model has been constructed in GETRAM of the northbound lanes of the study section of the Southern Motorway. It consists of the three motorway through lanes and a short length of each interchange ramp. Traffic has been applied to this network as three vehicle types (cars, LCVs and HCVs). A trip matrix was produced for each vehicle type during each 15 minute time period for an extended morning commuter peak from 6.00am to 10.00am. The traffic flows applied to the model are an approximation to the actual flows that existed on the motorway on Friday 26 September 1997. The raw traffic data measured in the field were manually adjusted to make up for several deficiencies including the lack of length classification on the ramps, under - counting due to equipment faults and the fact that the video classifier sites operated only for the middle 2 hours rather than the full 4 hour extended peak. These gaps in the field data were filled by comparison with flow data from other days and missing length classification percentages were assumed from the 1992 postcard survey.


An accident just south of the study area blocked lane 1 for about 5 minutes at 7.30am on the Friday morning. This resulted in a reduction in flows entering the study area and a corresponding increase in vehicle speeds. The effect was removed from the traffic entering the model by averaging the flow rates before and after the blockage period. After inputting the road sections and trip matrices the model was run and some parameters adjusted by trial and error to try to replicate traffic conditions observed in the field. Model Outputs Figure 4 shows total vehicle flows by lane at several points within the model. It can be seen during periods of low flow ( the left most, outside lane ) and lane 3 is little utilised. While this is normal drive behaviour the Figure 4 Model Spreads and Flows (5 minute intervals)

Greenlane On-Ramp

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Legend


Figure 5 Field Flow Rates (on 26 September 1998)

Figure 6 Speed-Flow Scatter Plots (5 minute intervals)

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Figure 7 Field and Modelled Speeds

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Figure 8 Figure 9 Aerial Photo and Model Outputs, north of Greenlane Interchange

Vehicle T ypes

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Figure 8: AIMSUN2 output showing five random seed replications of a weaving area with the auxiliary lane blocked from 8:00 to 8:15.

A


Run Times The 4 hour simulation of this 9 km section of northbound traffic took 9 minutes and 6 seconds to run in batch mode on a Pentium 166MMX personal computer.

Conclusion About The Author

The author leads Transit New Zealands Transportation Planning Section in Auckland. In this role he manages the transportation planning phases of new State highway and freeway projects in the Auckland urban area. This motorway simulation-modelling project is the subject of the authors Master of Engineering thesis at the University of Auckland.

Disclaimer

The opinions expressed in this paper are those of the author and do not necessarily represent those of Transit New Zealand.

Acknowledgements

Transit New Zealand funded the work described in this paper. Transportation Simulation Systems, Barcelona supported GETRAM and graduate student M. Kamruzzaman assisted collection and reduction of the field data. References ATS (1990). Acceleration/Deceleration Profiles at Urban Intersections. Report to Transit New Zealand, Australasian Traffic Surveys, Victoria, Australia. Barcelo, J. and Ferrer, J. L. (1997). An Overview of AIMSUN2 Microsimulator, Department of Statistics and Operations Research, Universitat Politchnica de Catalunya. 16 pp. Benekohal, R. F., (1997) Procedure for Validation of Microscopic Traffic Flow Simulation Models, Transportation Research Record No 1320, Department of Civil Engineering, University of Illinois, TRR 1320, pp 190 202. Bennett, C. R. (1993) Revision of Project Evaluation Manual Speed Change Cycle Costs, N. D. Lea International NZ Ltd., Doc. No 8341, 53 pp.


Bloomberg, L. D. and May, A. D. (1994). Simulation Modelling of the Santa Monica Freeway, California PATH Program, Institute of Transportation Studies, University of California, Berkeley, 104 pp. Boone, J. L., and Hummer, J. E. (1997) Calibrating and Validating Traffic Simulation Models for Unconventional Arterial Intersection Designs. Transportation Research Record No 1500, Department of Civil Engineering, North Carolina State University, TRR 1500, pp 184 192. Chang, G, and Kanaan, A. (1990). Variability Assessment for TRAF-NETSIM, Journal of Transportation Engineering, Dept. of Civil Engineering, University of Maryland, 116, No. 5. pp 636-657. Cragg, C. A. and Demetsky, M. J. (1995). Final Report: Simulation Analysis of Route Diversion Strategies for Freeways Incident Management, Virginia Transportation Research Council. VTRC 95-R11. Hua Heng, T. (1989) Simulation of Traffic Flow on Dense Urban Street Networks: A Study of the Calibration Requirements of the FHWA-Netsim Model for New Zealand Conditions, ME Thesis, Department of Civil Engineering, University of Auckland Ministry of Transport (1997) Vehicle Fleet Emissions Control Strategy for Local Air Quality Management. Stage l, New Zealand Ministry of Transport, 201pp. Liu, C. C., Kanaan, A., Santiago, A. J., and Holt, G. (1992) Macro Vs. Micro Freeway Simulation: A Case Study, ITE 1992 Compendium of Technical Papers. pp 497-501. Quadstone Ltd. (1996) Paramics, Wide-Area Microscopic Traffic Simulation, UK Motorway Validation Report. Quadstone Ltd. 25 pp. Rathi, Ajay K. (1992) The Use of Common Random Numbers to Reduce the Variance in Network Simulation of Traffic. Transportation Research. Part B, Methodological, Center for Transportation Analysis, Energy Division, Oak Ridge National Laboratory, 26B, No. 5, pp 357-363. Ross, P. and Gibson, D. (1997) Review of Road Traffic Network Simulation Models, Transportation Research Record, Federal Highway Administration - Office of Research and Development, TRR 644, 6 pp. Ruffell, J. and Ellery, G. (1998). Auckland Motorways Traffic Data Annual Report 1997, ATMS Data Collection PA1495. Serco Consultancy. 39 pp.


Statistics New Zealand (1997). 1996 Census of Population and Dwellings Final Counts. Transit New Zealand (1995). Auckland State Highway and Motorway Traffic Count Data 1995. Transit New Zealand. 50 pp. Transit New Zealand (1996). Highway Information Sheets Region 2 Auckland. Transit New Zealand. 28 pp. Transportation Research Board (1994). Highway Capacity Manual.

Wooton Jeffreys (1994). Motorway Lane Discipline: Motorway Traffic Modelling Study, Final Report Transport Research Laboratory, United Kingdom.

Montero, L, Codina E., Barcelo, J., and Barcelo, P (1998). Combining Macroscopic and Microscopic Approaches for Transportation Planning and Design of Road Networks. Department of Statistics and Operational Research, Technical University of California (Prepublication draft).

Wang, M., aqnd Cassidy, M (1995). Electronic Surveillance and Central System for Traffic Management on the Borman Expressway, Part 2- Calibrating A Simulation Model. Indiana Department of Transportation, Indianapolis, USA. 15pp.

Hughes, J. Intensive Traffic Data Collection for Simulation of a Congested Auckland Motorway (1998). Transit New Zealand, Auckland, New Zealand. 15pp (unpublished draft).

Auckland, New Zealand

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