Increasing the Highway Capacity without Additional Infrastructure

Copyright@ IFAC Technology Transfer in Developing Countries, Pretoria, South Africa, 2000

INCREASING THE HIGHWAY CAPACITY WITHOUT ADDITIONAL INFRASTRUCTURE

Marianne Vanderschuren ll , Ronald van Katwijk ll and Henk Schuurman u

*Netherlands Organizationfor Applied Scientific Research Institute of Infrastructure, Transport and Regional Development (FNO Inro) tel. 7 31-15269 6871,fax. -"- 31-152697782 e-mail [email protected]; [email protected] "Transport Research centre (A V11 Ministry of Transport. Public Works and Water Management te/. .:.. 31 - 10 282 5889,fax. +31 - 102825842 e-mail [email protected]

Abstract: While more and more ITS systems are invented and tested, TNO felt the need to investigate the effect of these systems on traffic flow. With support of the Dutch government a microscopic simulation model for freeway traffic was built, called MIXIC. It was designed to assess the impact of Intelligent Traffic Systems (ITS) on the level of a single road segment. The objective of this paper is to summarize the results of MIXIC-studies on road capacity. The paper concludes with a translation of the results to the South African situation. Copyrightr&> 2000 IFAC Keywords: Road traffic, Traffic control, Computer Simulation, Intelligent Cruise Control, Intelligent Control

I.

were transferred to another location; sometimes they were not solved at all. Because of latent demand the infrastructure investments only reduced the

BACKGROUND

More than 5 million cars drive an average of 16,000 kilometers a year in the Netherlands; and the reader has to keep in mind that the Netherlands is smaller that the Kruger National Park. And every year the mobility increases. With an average economic growth the mobility increase in the Netherlands is around 2% a year. As a much bigger country with fewer cars the mobility problems in South Africa were limited for a long time. Nevertheless in urban areas, like the Gauteng province, mobility increases rapidly. And the expectation is that the number of cars in South Africa will increase rapidly in the coming years.

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In the last decades Dutch governments and engineers tried to solve the congestion problems by building new roads and increasing the number . of lanes. Unfortunately, none of these investments solved the problems. Sometimes the problems

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135

Increase of the highway capacity

government a microscopic simulation model was buile. MIXIC is a microscopic simulation model for freeway traffic. It was designed to assess the impacts of Intelligent Traffic Systems (ITS) on the level of a single road segment.

Since ten years research organizations investigate the opportwlities for a better exploitation of +.e road network by using Dynamic T; .• Management (DTM) and other ITS syS , ~lS (Intelligent Transport Systems). It appeared that different DTM- and ITS-systems result in different exploitation effects. Figure 1 gives an illustration of the DTM possibilities: Special purpose lanes, Automated Vehicle Guidance and a redesign of the existing roads into more but narrower lanes. Figure 2 shows the estimated I effects and costs of DTM- and ITS measures (Koningsbruggen, van P.H., et al, 1999). Last year the Dutch government agreed that future investments would be in a better utilization instead of infrastructure.

Traffic performance, traffic safety, exhaust-gas emission and noise emission are analyzed by means of a detailed simulation of the interaction between the driver, his (intelligent) vehicle, the (intelligent) infrastructure and other vehicles. A detailed vehicle- and driver model forms the core of MIXIC (figure 3). These models are based on extensive theoretical and experimental study results.

Indication of the increase of capacity

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The models determine the speed, acceleration and lateral and longitudinal position (lane) based on the actual traffic state and inputs from the intelligent infrastructure (e.g. beacons transmitting speed limits). The vehicle can contain intelligence (like Adaptive Cruise Control) which influences the vehicles and driver's behavior.

The effects shown in figure 2 are estimated by experts. In the Netherlands there are also computer models available to study these effects. In this paper the results of simulations using the MIXIC model will be described. The paper concludes with a translation of the results to the South African situation.

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MODEL CONTENT

MIXIC contains various sub-models (figure 3). These are evaluated on a fixed discrete time basis, currently 0.1 second. Other time steps may be chosen but may require re-calibration of certain sub-models. The vehicle model contains the dynamics of the vehicle. It translates the position of gears and of the acceleration and brake pedal into a driving force. The driver model determines

MIXIC: MICROSCOPIC SIMULATION OF INTELLIGENT VEHICLES AND INFRASTRUCTURE

While more and more possible systems were invented and field-tested, TNO felt the need to investigate the effects of these new systems on traffic flow. With support of the Dutch

3 MIXIC is developed in co-operation with four TNO institutes: TNO Human Factors Research Institute, TNO Road-Vehicles R.cscarch Institute TNO Institute for Applied Physics and TNO wo. It is co-funded by the Transportation Research Centre of the Dutch Ministry of Transport, Public Worb and Watcr Management.

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Figure 3 General structure ofMIXIC

Figure 2 Estimated effects ofDTM and ITS measures2

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TRI = Traffic Regulating Installation DRIP = Dynamic Route Information Panel

136

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the desired acceleration based on the desired driver speed and the positions of other vehicles Hence gear, currently surrounding him. acceleration- and brake pedal positions are calculated which then are fed into the vehicle model. The driver model also contains lane change logic, both for free- and mandatory lane changing. The former applies for speed gain in normal driving, the latter occurs in the case of lane drops or lanes dedicated to other road users.

THE MIXIC TOOL IN USE

MIXIC has been applied so far for research on the impact of Adaptive Cruise Control (ACC) in different settings, reduction of the lane widths (the so-called dynamic road profile ), long truck combinations (so-called 'road-trains'), and the effects of introducing a fog-warning system. In these studies MIXIC was used as a tool for 4 assessment of impacts on the road segment level . The main objective of this paper is to summarize results of MIXIC studies on road capacity. Moreover, available results on road safety and energy consumption are also mentioned.

The vehicle intelligence model incorporates systems that support the driver in his driving task. Currently controllers for Adaptive Cruise Control (ACC), Intelligent Speed Adaptation (ISA) and Co-operative Following (CF) are implemented in the model. The vehicle intelligence model can function autonomously (e.g. ACC), based on communication between the vehicle and the intelligent infrastructure (e.g. ISA) or on mutual communication between vehicles (e.g. CF). The intelligent infrastructure model represents intelligent roadside systems, which monitor the traffic situation and actuate measures meant for influencing driver or vehicle behavior. In the current version of MIXIC the ISA beacons, which transmit a speed limit based on the observed traffic state, are the only examples.

Adaptive Cruise Control Adaptive Cruise Control is an extension of the conventional Cruise Control, which automatically adapts the speed and keeps a safe distance in carfollowing situations. In different studies the effects of ACC have been explored in different settings. These situations were: • ACC on a straight highway without discontinuities (1.5 sec); • ACC on a straight highway without discontinuities with short headways (1 sec) • ACC with a bottleneck (lane drop); • Bottleneck with a dedicated lane for ACC vehicles (vehicle headways of 1 sec) and • Bottleneck with a dedicated lane for ACC vehicles and short headways (vehicle headways of 0.7 sec).

MIXIC collects information about individual vehicles and traffic flow. These data are stored in files for analyze purposes. Various post processing modules aggregate and transform these data into user friendly information on exhaust-gas and noise emission, safety indicators (like shock wave information and headway distribution) and performance indicators (like throughput). MIXIC was calibrated for the Dutch situation (motorways). Figure 4 gives an impression of MIXIC's graphical output.

In the first two studies the MIXIC model was used to analyze highway traffic flow with ACe. The impacts were assessed using as input real traffic measurements for different levels of traffic (up to 7000 vehlh for a 3-1ane motorway). The study has confirmed the notion that ACC systems can contribute to a more stable traffic flow without sacrificing capacity: the simulation only showed a slight decrease of the average speed. However with high penetration levels (up to 40%) and an ACC target headway of 1.5 seconds the traffic performance deteriorated: at high traffic volumes speeds collapsed on the left lane. The conclusion from the first evaluations was that ACC may be introduced without any problems for traffic performance, but with high penetration levels shorter headways are needed (Arem, van B., et al, 1995). The next step in the analysis of traffic flow with ACe was to introduce a bottleneck: a 4-1ane highway was reduced to a 3-lane highway. Traffic performance and safety criteria were compared for

Figure 4 MIXIC's graphical output

In combination with a network model (DYNDART) the MIXIC output concerning road segment allows analysis of ITS systems on a network level. So far, this has only been done as a test case for ACC.

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the case without and with ACC. The most important criteria were: • the number of removed vehicles; In case the simulated vehicles physically overlap, one of the vehicles involved is removed from the simulation; since the MIXIC model is not meant to simulate incidents. • the number of shock waves; shock waves caused by strong deceleration vehicles are an indication of non-homogeneous traffic; • the average speed of the vehicles; • the average travel time and • the average traffic volume (5-minute aggregates).

platooning on dedicated lanes, the so-called automated highway system (AHS) Ioannou, P.A., 1997): • Co-operative Following (CF), based on intervehicle communication: the rationale behind this extension to the ACC functionality is that vehicles involved in a shock wave warn upstream traffic for sudden deceleration; other vehicles capable to receive this warning can anticipate to the upcoming disturbance, thus damping the shockwave; • Intelligent Speed Adaptation (also: External Cruise Control), based on communication between the vehicle and the infrastructure: the rationale here is that the infrastructure monitors upcomiog traffic demand and transmits an appropriate speed limit using beacons; vehicles receiving this signal are forced to adapt their speed, thus smoothening traffic flow and facilitating merging maneuvers for instance upstream of a lane drop.

The results for these variables for the different simulations are given in table I . Table I Traffic performance variables for the ACC simulationss ~ '" o> 11)

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The first tests with the simulation of these systems turned out problematic: the CF controller appeared to be unstable (due to overreaction and interference with the ACC logic) while the ISA controller was stable but reduced the capacity at a bottleneck, which is an undesired effect (Alkim, T.P., et al, 2000) Future research will try to overcome these shortcomings.

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Dynamic road profile The DTM measure 'Dynamic Road Profile' envisages a dynamic redesign of the road profile dependent on prevailing traffic demand. At low traffic volumes the traffic is processed on a normal cross-section, e.g. 2 lanes of normal width for each driving direction. At high volumes the same stretch of road pavement is divided into narrower lanes so that space comes available for an additional lane without sacrificing the emergency shoulder lane. This measure can be combined with a lower speed limit and an overtaking prohibition for lorries to avoid negative safety consequences.

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Introducing ACC in the bottleneck clearly shows that the number of removed vehicles and shock waves decreased. This is the first indication of a better traffic performance and a decrease of the likelihood for incidents. Although the average speed decreases, a difference in travel time is hardly observed. Moreover, the traffic volumes increase (with highest observed 5-minute intensity of 8160 pcuIh for 50% ACC with special lane and short headways, compared to 7570 pcu/h in the reference case without ACC). It can be concluded that the capacity and traffic performance at a bottleneck caused by a lane drop increases when ACC is introduced, especially in combination with special lanes (Arem, van B., et al, 1997a).

MIXIC was used to analyze traffic flow on the new design with narrow lanes. For this purpose the model was extended with behavioral rules that reflect drivers' behavior in the nearby presence of vehicles in adjacent lanes, with overtaking prohibition for trucks and with adapted speed choice in the case of speed limits lower than the usual 120 kmIh. Drivers may for instance abort a passing maneuver when a truck in the adjacent lane comes too close.

The next step in the series of studies to examine to what extent communication can contribute to a safer and more efficient traffic flow. Specifications were developed to simulate two variants of the introduction of communication in traffic flow with autonomous vehicles in mixed traffic (as opposed to

The extended - but only partially calibrated - model was applied in an exploratory study in which different variants of redesigned road profiles with different speed limits were tested. The outcome showed no particular difficulties of traffic flow on narrow lanes: capacity increases of up to 30% were simulated, which is comparable to an additional lane

Results on the last simulated link (average for the whole simulation period) 138

of normal width (Tamper-e, C.MJ., 1999). The Ministry of Transport is now examining the possibility to collect real-world calibration data that would enable to validate these encouraging preliminary results.

at a penetration level of 25% of ACC vehicles (Vanderschuren, M.J.W.A , et al, 1997).

Road-trains Increasing the capacity of (freight) vehicles will result in an increase of the road capacity in that the amount of passengers or goods that can be transported increases (when the same number of vehicles can be processed).

Estimations on the potential impact of Dynamic Traffic Management and ITS systems are promising. Analysis with the microscopic simulation model MIXIC show that a positive impact on throughput, traffic safety and on the energy consumption are foreseen. Although the MIXIC model that was used to assess these systems was calibrated for the Dutch situation, it is expected that the findings from the Dutch studies are also valid for other countries. Moreover, the MIXIC tool can be adapted to other situations.

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Before road trains can be introduced a lot of research has to be done. Changing the vehicle size might not only influence the road capacity, but also safety (the sight from the vehicle and the capability to stop will change) needs to be investigated.

CONCLUSIONS

In South Africa only one on five adults owns a car. Comparing to many other countries this is very low. It can be concluded that the number of new potential car owners is very high. The current infrastructure and measures used in South Africa will be insufficient for potential mobility growth. Measures investigated for the Dutch situation can help to solve current and future problems on African roads.

In a simulation study using MIXIC the percentage freight vehicles was substituted by road trains (Hoogvelt, R.BJ. , et al, 1996). The results of the simulation study are encouraging. Keeping the same traffic patterns and throughput is possible while the (freight) transport capacity increases. The study showed: • no significant decrease of the speed in any of the simulations; • no significant change of the number of shock waves; • no significant change of the throughput and • no negative influence on overtake maneuvers.

The cost!benefits of an investment can be very different as shown in figure 2. Although the figure gives an estimation of the situation in the Netherlands, the approach can be used for the South African situation. Europe experienced that investments in infrastructure are not the answer to mobility growth. South Africa has the opportunity to optiroize their investments from the start.

Fog-warning system Although in absolute sense fog is not a dominant factor in traffic safety, traffic can be seriously hindered and endangered by the presence of fog. Accidents are often caused by the inability of drivers to anticipate to the situation ahead and an inadequate adjustment of the speed choice of drivers. Fogwarning systems warn drivers for reduced visibility and display a maximum speed that is adapted to the severeness of the circumstances. A specific mathematical behavioral model was developed and applied in MIXIC. The assessment revealed that the presence of a fog-warning system had a positive impact on both traffic performance and traffic safety because speed differences between drivers were strongly reduced (Voort, van der M.C., 1996).

REFERENCES

Alkim, T.P., H. Schuurman & C.MJ. Tamper-e, Effects of External Cruise Control and Cooperative FollOWing on Highways: an Analysis with the MIXIC Traffic Simulation Model, Proceedings of the 2000 Intelligent Vehicles Conference, The Ritz-Carlton Hotel, Dearbom, MI, USA, October 4-5, 2000 van. J.H. Hogema, MJ.W.A Arem, B. Vanderschuren & C.H. Verheul, (1995). An assessment of the impact of Autonomous Intelligent Cruise Control, November 1995, Report INRO-VVG 1995-17a Arem, B. van, AP. de Vos & MJ.W.A Vanderschuren, (l997b). The microscopic traffic simulation model MIXIC 1.3, TNO-report, InroVVG 1997-02b, Delft, January 1997 Arem, B. van. AP. de Vos & MJ.W.A Vanderschuren, (1997a), The effect of special lanes for intelligent vehicles on traffic flows , An exploratory study using the microscopic traffic simulation model MIXIC. TNO-report, Inro- VVG 1997-0280 Delft, March 1997

Impact on energy consumption ofACC In addition to the series of research on the effect of ACC with an interest in traffic performance and safety, also the impact of ACC on energy consumption was analyzed. The simulations show that ACC drivers consume less fuel because of the smoother following behavior: they react earlier to speed difference with their predecessor so that less shock waves emerge in traffic flow. An interesting result is that the more homogeneous traffic flow causes both ACC and non-ACC vehicles to reduce energy consumption: up to 10% and 5% respectively 139

Hoogvelt, RB.J., U .M. Besseling, J. van Honk, R Elink Schuurman, C. W. Klootwijk, P.M.A Slaats, P. Hooijdonk, B. van Arem & AJ. Vieveen (1996), Langere en zwaardere vrachtwagens, 1NO wr report 96.0R VD.068.11RH., Delft 1996 Ioannou, P.A (ed.), (1997), Automated Highway Systems, University of Southern California, Center for Advanced Transportation Technologies, Plenum Press, New York, 1997 Koningsbruggen, P.H. van, M. Westerman, E.A Berghout, AJ. Vieveen & R W. Feenstra, (1999), Integrale Benutting gebied Den Haag-Gouda, TNO-report (in Dutch), VKI999-08, Delft, November 1999 Tampere, C.M.J., (1999), Dynamische Dwarsprojielen: een verkenning van de verkeersafwilkkeling op smalle stroken, Een verkennende simulatiestudiemet het microscopisch model MIXlC, TNO-report (in Dutch), VKl999-04 Vanderschuren, MJ.W.A, B.van Arem & G.F. Zegwaard, (1997), Energievriendelijke Variabele Snelheidsbeheersing (EVS); een toepassing van het micro simulatiemodel MIXlC, 1NO-report (in Dutch), InroNVG 1997-18, Delft, December 1997 Voort, M.C. van der (1996). An assessment of the impact of fog-warning systems (graduate assignment report). University of Twente, Enschede, 1996

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