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Congestion Transport Netherlands

Fighting Congestion in The Netherlands

Two measures reviewed

1. Introduction

Congestion is a large and increasing problem in The Netherlands. Although both local and national governments are trying to improve the reliability of travel times, the real problem remains: a daily mismatch between supply and demand. This research will give an overview of why congestion is a problem for the Netherlands, what the main reasons for congestion are and what type of measures can be taken. From two measures that have been considered in The Netherlands -reducing the maximum speed and prohibiting freight transport during rush-our on Dutch Highways-, it is determined what their costs and benefits are (both direct and indirect), what the change and consequences are of their induced demand, and what their effects during rush-our are, in terms of flow, speed and density, when they are modelled and observed in Greenshield's macroscopic model.

1.1 Congestion

According to literature, congestion relates to an excess of vehicles on a portion of roadway at a particular time resulting in speeds that are slower than normal/free flow speeds. (Cambridge Systematics Inc., 2005). The Dutch Government defines three different kinds of congestion:

Slow traffic → traffic that drives between 25 and 50 km/h for at least 2 kilometres.

Stagnant traffic → traffic that drives between 0 and 25 km/h for at least 2 kilometres.

Slow to stagnant traffic → slow traffic over larger stretches of road with small stretches of stagnant traffic (Verkeersinformatiedienst 2008).

In this research, congestion is defined as speeds slower than 50 km/h for at least two kilometres, because the focus of this research is on The Netherlands and speeds between 50 km/h and “free flow speeds” are not defined as congestion in The Netherlands.

But why is it important to reduce congestion at all? Congestion and reduced reliability of travel time has a large impact on both regional and national economies. In the short term, the mode or time of travel might change, but over the longer run, congestion can also influence decisions about where to live and work or where to start a business. According to the Dutch Ministry of Transport and Public Works, there are four important victims of congestion, which all influence the Dutch economy:

Trucking sector → Congestion causes longer travel times and less reliable pickup and delivery times for truck operators. To compensate, motor carriers add vehicles and drivers and extend their hours of operation, eventually passing these additional costs along to shippers and consumers.

Impacts on Businesses → Congestion increases the costs of delivering goods and services, because of the increased travel times and operating costs incurred on the transportation system. Less obviously costs of congestion for businesses are:

The costs of remaining open for longer hours to process late deliveries.

Penalties or lost business revenue associated with missed schedules.

Costs of spoilage for time-sensitive deliveries.

Costs of maintaining greater inventory to cover the undependability of deliveries.

Costs of reverting to less efficient production scheduling processes.

The additional costs incurred because of access to reduced markets for labour, customer, and delivery areas.

Households → Households have both financial budgets and what is termed “time budgets” that are both impacted by congestion. Households plan their activities around the available time budget as well as around their financial budgets. As vehicle operating and maintenance costs increase with rising congestion, the budget for some types of activities or expenditures decreases. The perceived “quality of life” of a neighbourhood is diminished as well, when the safety, reliability and the convenience of the transportation system decreases.

Regional economies → Regional economies are affected by these household and business-specific impacts. Diminished cost competitiveness and market growth opportunities are equivalent to a reduced ability to retain, grow, and attract businesses. Additionally, the redistribution of business and household activity to outlying areas and the direct delay for trips that are not diverted or otherwise changed both lead to decreases in air quality, increases in public infrastructure investment requirements, and potential impacts on health and quality of life factors. (Ministerie van Verkeer en Waterstaat, 2008; Cambridge Systematics, Inc., 2005)

Congestion can occur due to several reasons, and it is often not just one cause that results in congestions. Most of the time it is more like a chain of events that cause congestion. Nevertheless, field-observations have defined seven root causes of congestion:

Capacity shortage (also called bottlenecks): the maximum amount of traffic capable of being handled by a given highway section is exceeded.

Traffic incidents: events that disrupt the normal flow of traffic, usually by physical impedance in the travel lanes.

Work zones: construction activities on the roadway that result in physical changes to the highway environment.

Weather: environmental conditions that lead to changes in driver behaviour that affect traffic flow.

Traffic control Devices: intermittent disruption of traffic flow by control devices such as railroad grade crossings and poorly timed signals.

Special events: this is a special case of demand fluctuations whereby traffic flow in the vicinity of the event will be radically different from “typical” patterns. Special events occasionally cause “surges” in traffic demand that overwhelm the system.

Fluctuations in normal traffic: Day-to-day variability in demand leads to some days with higher traffic volumes than others (Wabe, 1970, Schijndel and Dinwoodie, 2000 and Verkeersinformatiedienst 2008).

The interaction between multiple sources is complex and varies greatly from day-to-day and highway-to-highway. The problem is that, with the exception of capacity shortage, the sources of congestion occur with maddening irregularity; nothing is ever the same from one day to the next. And if the congestion picture was not complicated enough, one must also consider that some events can cause other events to occur. In addition to causing delay to travellers, the sources of congestion also produce another effect: variability in congestion conditions. This variability in congestion is known as travel time reliability, in other words, how “reliable” travel conditions are day-to-day. The Dutch Government is currently focussed on improving travel time reliability rather that decreasing congestion in The Netherlands.

1.2 Research questions

The Dutch government has proposed several measures the last years, also aiming at a reduction of congestion but mainly focussed on increasing the reliability of travel time. This paper will review two proposed measures aiming at a reduction of congestion; reducing the maximum speed to 80 km/h and prohibiting freight transport during rush-our on Dutch Highways.

The first measure, reducing the maximum speed to 80 kilometres per hour on Dutch highways, is already tested in real life, so reviewing this measure will focus on the question if the reduction of the speed caused a decrease or increase in congestion and if this decrease or increase could be predicted with Greenshield's model. Besides this, also an overview will be given of the direct- en indirect costs, and its induced demand.

The second measure, prohibiting freight transport during rushes-our on Dutch Highways, is already considered in 2005 by the Dutch Government, but not actually implemented. This paper will give an overview of its effects in terms of cost, benefits, induced demand, flow, speed and density (according to the model of Greenshield), when it will be introduced. Also a prediction of the usefulness in fighting congestion will be given.

To give this research a clear structure, the following research question is formulated:

What are the consequences of the two proposed measures -reducing the maximum speed and prohibiting freight transport during rush-our on Dutch Highways- in terms of costs, benefits, induced demand, flow, speed and density and can they decrease congestion in The Netherlands?

This paper is organized as follows: Section 2 will give some theoretical background of the Greenshield model, Section 3 will give the effects (in terms of cost, benefits, induced demand, flow, speed and density) of the two congestion-reducing-measures based on literature and the Greenshield model, and section 4 will give a conclusion and discussion.

2. Traffic flow theory

Traffic flow theory is a tool that helps understand and express the properties of traffic flow. Speed, flow, and density are the most important variables in traffic flow theory and all are related to each other. The relationships between speed and density are not difficult to observe in the real world, while the effects of speed and density on flow are not quite as obvious.

Under uninterrupted flow conditions, speed, density, and flow are all related by the following equation:

(1)

where q is the flow in vehicles per hour, v is the speed in kilometres per hour and k is the density in vehicles per kilometre.

Because flow is the product of speed and density, the flow is equal to zero when one or both of these terms is zero. It is also possible to deduce that the flow is maximized at some critical combination of speed and density.

Greenshield was able to develop a model of uninterrupted traffic flow that predicts and explains the trends that are observed in real traffic flows. Greenshield made the assumption that, under uninterrupted flow conditions, speed and density are linearly related.

(2)

where A & B are constants determined from field observations.

Determining A and B is normally done by collecting velocity and density data in the field, plotting the data, and then using linear regression to fit a line through the data points. The constant A represents the free flow speed, while A/B represents the jam density Inserting Greenshield's speed-density relationship (2) into the general speed-flow-density relationship (1) yields the following equations and figure 2:

. (3)

This new relationship between flow and density provides an avenue for finding the density at which the flow is maximized and with that the relationship between flow and speed. Summarized, the following can be derived from Greenshield's model:

When the density is zero, the flow is zero because there are no vehicles on the roadway.

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As the density increases, the flow also increases to some maximum flow conditions.

When the density reaches a maximum, generally called jam density, the flow must be zero because the vehicles tend to line up end to end (parking lot conditions).

As the density increases the flow increases to some maximum value, but a continual increase in density will cause the flow to decrease until jam density and zero flow conditions are reached (Loa, S.C, and H. J. Cho, 2005; Alkemade 2008)

Greenshield's model has also some disadvantages. First of all, Greenshield never worked with freeway data but only with single lane traffic. Nevertheless, currently his model is commonly used for freeways. Secondly, by current standards of research the method of analysis of the data, with overlapping groups and averaging prior to curve-fitting, would not be acceptable. Finally, the model was based on data of holiday traffic, while current work often uses this model for regular commuters who are familiar with the road and know what a road is capable of, which influences their behaviour. Despite the disadvantages of this model, it is still commonly used because of its simplicity and because of a flaw on better models (Oregon State University, 2008).

I will use Greenshield's model to determine the effects of reducing the maximum speed and prohibiting freight transport during rush-our on the speed, flow and density during rush-our. It will not be more than an approximation of reality, but nevertheless will give insight in the effects of the two measures.

3. Results

This chapter will give the results of the two proposed congestion-reducing-measures, in terms of its costs, benefits (both direct and indirect), induced demand, flow, speed and density (according to Greenshield's model). Section 3.1 will give the results of the first measure: reducing the maximum speed to 80 km/h on Dutch highways. Section 3.2 will give the results of the second measure: prohibiting freight transport during rush-our on Dutch Highways. These results will be compared with a hypothetical, single lane road H₀, representing a Dutch highway-section with an average maximum flow of 4000-5000 vehicles per hour and a free-flow speed of 120 km/hour (Huijbregts and Rozemeijer, 2005).

3.1 Reduction of the maximum speed

The main reason for reducing the maximum speed, initially on four different sections of highways in The Netherlands, was improving the air quality. But with this reduction of speed it was supposed, that congestion should also decrease, because the Dutch Ministry of Transport and Public Works assumed that a decrease of speed would cause less accidents and a more constant flow (Havermans et al. 2006). Evaluation the 80 km/h zones on the Dutch highways gives the following indirect and direct costs and benefits:

Costs	Benefits
Increase of congestion with 11-69% (Havermans et al. 2006).	Reduction of the emissions (NO_x & fine materials (fijnstof)) (Havermans et al. 2006).
	Reduction of sound-pollution with 0-1,5 Decibels. Also the sound-peaks decreased (Havermans et al. 2006).
	Increase of safety (Havermans et al. 2006).

Induced demand was not observed, because supply never increased.

According to Greenshield's model, the following happens, when the free flow speed on highways is reduced from 120 km/h to 80 km/h, with a jam density of 158 vehicles/kilometre:

3.2 Prohibiting freight transport

Implementing measure two, prohibiting freight transport during rush-our on Dutch Highways, will give the following indirect and direct costs and benefits:

Costs

Benefits

Because truck-drivers are not allowed to enter highways during certain time-periods, they will have to start earlier or work later, which will cause some additional costs (Huijbregts and Rozemeijer, 2005).

Decrease of congestion during rush-our (Huijbregts and Rozemeijer, 2005).

Increase of safety, because of lower speed-differences (Verkeersinformatiedienst 2008).

Indirect benefits for businesses, caused by the decrease of congestion (Ministerie van Verkeer en Waterstaat, 2008; Cambridge Systematics, Inc., 2005).

Regional and national economies benefit from less congestion (Ministerie van Verkeer en Waterstaat, 2008; Cambridge Systematics, Inc., 2005).

Induced demand is expected, because supply increases. Prohibiting trucks on the highway will increase the capacity of the highway. How much induced demand is expected is difficult to estimate, but with Greenshield's model it is possible to estimate which induced demand can be dealt with.

When freight transport is prohibited during rush-our on Dutch Highways, the jam density will increase from 158 to 200 vehicles per kilometre.

, that the maximum density increases, which cause an increase of the maximum flow (from 4740 to 6000 vehicles/hour) in that the maximum flow will occur with the same speed as in the normal situation (60 km/h), but the flow will be much larger. With an assumed range of maximum flow between the 4000 and 5000 vehicles, this model shows a decrease of congestion. Even with a maximum flow of 5000 vehicles per hour, the road is still capable of an additional (induced) 1000 vehicles per hour, and allows a speed above defined as congestion.

Additionally I have tried to find reasons why truck operators currently do not avoid rush-our. One of the main reasons for this is the position of power of the truck operators. The clients are dominant in deciding when deliveries have to take place, rather than the knowledge of the truck operator about most optimal time frames for deliveries.

4. Conclusion and discussion

The results of this paper clearly state the effects of the two congestion-reducing-measures. However it must be hold in thoughts that direct and indirect effects are solely based on literature and that Greenshield's model is not meant to be used for these kind of predictions on highways and is only used because of it simplicity and because of a flaw on better models.

Measure one -reducing the maximum speed to 80 km/h on Dutch Highways - did manage to improve air-quality and sound-pollution, and increased safety, but caused a major increase in congestion. This increase in congestion can also been observed, when this measure is modelled with Greenshield's model. Because of the positive effects of this measure, the Dutch Government is still holding on to this measure, but is considering to increase the maximum speed to 100 km/h, which will increase the maximum flow. This could help diminish the increase of congestion, but according to Greenshield's model will not reduce congestion in The Netherlands.

Measure two -prohibiting freight transport during rush-our on Dutch Highways- will increase the maximum flow on the road, and thus decrease congestion. Even when induced demand causes a raise of 20% of flow, the capacity of the road is still able to allow speeds above defined as congestion. Although this measure has negative effects on truck operators, it also gives truck operators a better change in discussing delivery times with their clients. If transportation during rush-our is not allowed by law, it is more likely that delivery times will be adjusted, which is in return beneficial for the truck operators. Overall, it must be concluded that this measure will help reducing congestion in The Netherlands.

References

Alkemade, F. 2008. Lecture notes of the course: Analysis of Transport System Dynamics (GEO2-2246). Obtained at 18 March 2008.

Cambridge Systematics, Inc. 2005. Traffic Congestion and Reliability; Trends and Advanced Strategies for Congestion Mitigation. Final Report.

Havermans, P., O. Tool. H. Bokma and H. Stoelhorst. 2006. Evaluatie 80 km zones. Ministerie van Verkeer en Waterstaat. http://www.rijkswaterstaat.nl/dvs/Images/15636_tcm178-145059.pdf. (Accessed 04 September 2009).

Huijbregts, P.J.M. and S.P.J. Rozemeijer. 2005. Het vrachtverkeer in de spitsperioden op het hoofdwegennet. De adviesdienst Verkeer en Vervoer, http://www.verkeerenwaterstaat.nl/ken nisplein/uploaded/AVV/2006-07/319940/vrachtverkeer%20spits%20412229.pdf (Accessed 04 September 2009).

Loa, S.C, and H. J. Cho. 2005. Chaos and control of discrete dynamic traffic model. Journal of the Franklin Institute, Vol. 342, p. 839-851.

Ministerie van Verkeer en Waterstaat. 2008. Value of Time goederenvervoer: Alle modaliteiten; Basisjaar 2006. http://www.rws-avv.nl/pls/portal30/docs/16435.PDF (Accessed on 04 September 2009).

Oregon State University, 2008. Transportation Engineering; Online lab manual, Chapter 6, Section 3. http://www.webs1.uidaho.edu/niatt_labmanual/ (Accessed on 04 September 2009).

Schijndel W. J. van and J. Dinwoodie. 2000. Congestion and multimodal transport: a survey of cargo transport operators in the Netherlands. Transport Policy, Vol. 7, Issue 4, p. 231-241.

Verkeersinformatiedienst. 2008. Chapter 2: Achtergrond, Section 3: Fileproblematiek. http://www.verkeersinformatiedienst.nl/fileproblematiek.html (Accessed 04 September 2009).

Wabe, J.S. 1970. Congestion and the Speed of Traffic on Trunk Roads. Applied Statistics, Vol. 19, No. 1, p. 42-49.

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