Speed

This report examines the impacts of passing lanes on safety, journey time and user experience and provides guidance to assist in the development of passing lane installation projects.

The research found that passing lanes result in safety benefits, including perceived safety by motorists, safer operational conditions, and historical crash reductions. Passing lanes were also found to improve journey times through a small increase in travel speed and a significant reduction in percentage of time spent following a slower vehicle.

This project included a

• literature review
• safety analysis, before-and-after analysis of crash records, speed and headway analysis, and overtaking behaviour analysis
• journey time analysis, including development of modelling guidance and numerical experiments on the impact of passing lanes on travel speed and per cent time spent following
• road user experience survey analysis, including an analysis of perceptions and valuation of level-of-service
• a review and re-calibration of the TRAffic on Rural Roads (TRARR) model.

TRB Webinar on speed and fuel consumption relationship.

Road and traffic factors that influence speed have attracted interest for modeling vehicle
fuel consumption and traffic safety. While the influence of geometric design and traffic operation factors have been well studied, the relationship between pavement surface roughness and vehicle speed was not well established in previous studies, mainly because of data limitations. The authors applied a new dataset, Naturalistic Driving Study (NDS) data of the Strategic Highway Research Program 2 (SHRP 2), for studying the roughness-speed relationship. A method was developed to identify the roughness-speed relationship under different road and traffic conditions. The International Roughness Index (IRI) was employed to describe the pavement roughness condition in this research. The new method can be applied to reveal a roughness-speed relationship of different road and traffic conditions with the SHRP 2 NDS dataset. The case study shows that pavement surface roughness impacts traffic speed for different full-access control road scenarios with the listed properties. The research conducted will give other engineers a guidance on how to develop a method to predict speed at different Levels of Service (LOS) given similar road properties on an all access roadway.

The article deals with impacts of road capacity on traffic speed and road user costs. Calculation of free speeds and operating speeds in the Highway Design and Management HDM-4 model are explained. In a case study, decrease of traffic speeds and increase of road user costs are observed if free flow capacity, nominal capacity and ultimate capacity is changed. Methodology for Annual Average Daily Traffic recalculation into passenger car space equivalent/lane/hr is explained. The article gives an insight into traffic flow modeling based on the ISOHDM study.

The effect of grade is very significant on traffic flow characteristics. While negotiating a particular upward gradient, the vehicles will have to overcome the grade resistance additionally along with other resistances (air resistance, rolling resistance and resistance due to inertia). An important aspect of road geometry design is the provision of safe distances to allow vehicle to change speed from low or moderate speed flows to high speed flows, and vice versa. On level roads, vehicles may maintain their speeds uniformly. However, on upgrades, heavy vehicles such as trucks, will experience significant reduction in their speeds, whereas, passenger cars and other smaller vehicles such as motorized-two-wheelers may experience relatively lesser speed reduction. Similarly, the effect of gradient on truck performance on upgrade and downgrade are not same. One of the approaches, to estimate the acceleration rates of truck for different grades to develop the various traffic flow models. The main objective of this study is to study the effect of weight-to-power ratio on truck accelerating characteristics for varying magnitude of gradients. The outcome of the study is expected to explain the dynamics of trucks on grades.

To investigate the impact of changes in pavement roughness on speed, this study developed a linear regression model to estimate free-flow speed on freeways in California. The explanatory variables include lane number, total number of lanes, day of the week, region (district), gasoline price, and pavement roughness as measured by the international roughness index (IRI). Data on the California freeway network from 2000 to 2011 were used to build the model. The results showed that pavement roughness has a very small effect on free-flow speed within the range of this study. For the IRI coverage in this study (90% of the records have an IRI of three m=km or lower and 90% of the records have an IRI change of two m=km or lower), a change of IRI of one m=km (63 in:=mi) results in a change of average free-flow speed of approximately 0.48–0.64 km=h (0.3–0.4 mph). This result indicated that making a rough segment of pavement smoother through application of a maintenance or rehabilitation treatment will not result in substantially faster vehicle operating speeds, and therefore the energy and emission reductions gained from the reduced rolling resistance will not be offset by increased fuel consumption from faster speeds. However, developing a good model to predict the free-flow speed was not fully successful. The southern California interstate freeway model yielded the best result with an adjusted R-squared of 0.72. For the rest of the regions in the state, the selected explanatory variables can only explain approximately half of the total variance, indicating there are still other variables with more substantial effects on free-flow speed that were not covered in this study

Traffic characteristics of a roadway are influenced by various factors like surface type, shoulder and roadway width, terrain, driver skills, side friction or side activities, road maintenance, etc. However for urban roads, the impact of side frictions i.e. bus stops, encroachments, on-street parking, etc. is much significant than any other factor. The extent of on-street parking and encroachment is generally high in developing countries where many activities often take place at the edge of urban roads. Their impact on traffic characteristics can be minimized by imposing few restrictions. Conversely bus stops are to be constructed by the authorities at different locations near or at the edge of urban roads. Bus stops are the designated places where passengers board and alight public transport buses. Different types of bus stops like curbside stops, bus bays, queue jumpers and nubs have significant effect on traffic flow. This paper reviews the literature on the effect of curbside and bus bay stop on urban traffic characteristics. It has been observed that presence of a bus stop ominously reduces the stream speed and capacity of an urban road. The present paper also suggests few areas where further work can be taken up by the researchers.

Vehicle speed–roughness relationship has a significant research gap in life cycle assessment model. The current available models describing the roughness effect on vehicle speed are very limited and outdated. In this paper, 32 individual pavement sections, each of which has roughness data of up to 8 years, were selected to develop the model. The roughness data cover a wide range, and the selected pavement sections contain both flexible and rigid pavement types and various numbers of lanes. Involved regression variables include the following: vehicle speed, roughness, volume–capacity ratio, pavement type, number of lanes and speed limit. Analysis of variance was first performed, indicating that pavement type and speed limit are not significant factors influencing the average vehicle speed. Following, strict statistical technique was used to correct the unobserved heterogeneity during the regression using a one-way fixed random model. The obtained regression model reveals that the average vehicle speed decreases 0.0083 mph with every 1 in/mi increase of the roughness ( − 0.84 km/h per m/km).

Study of the basic traffic flow characteristics and clear understanding of vehicular interactions are the pre-requisites for highway capacity analysis and to formulate effective traffic management and control measures. The road traffic in India is highly heterogeneous comprising vehicles of wide ranging physical dimensions, weight, power and dynamic characteristics. The problem of measuring volume of such heterogeneous traffic has been addressed by converting the different types of vehicles into equivalent passenger cars and expressing the volume as Passenger Car Unit per hour (PCU/h).

 

Computer simulation has emerged as an effective technique for modeling traffic flow due to its capability to account for the randomness related to traffic. This paper is concerned with application of a simulation model of heterogeneous traffic flow, named HETEROSIM, to quantify the vehicular interaction, in terms of PCU, for the different categories of vehicles, by considering the traffic flow of representative composition, on upgrades of different magnitudes on intercity roads in India.

 

The PCU estimates, made through microscopic simulation, for the different types of vehicles of heterogeneous traffic, for a wide range of grades and traffic volume, indicate that the PCU value of a vehicle significantly changes with change in traffic volume, magnitude of upgrade and its length. It is found that, the change in PCU value of vehicles is not significant beyond a length of 1600 m on grades. Also, it has been found that the PCU estimates are accurate at 5% level of significance.

In developing countries including India, mixed traffic condition prevails on roads and highways. There is a wide variation in the static and the dynamic characteristics of different types of traffic. The only way of accounting for this non-uniformity for any traffic analysis in traffic stream is to convert all vehicles into a common unit and the most accepted unit for this purpose is passenger car unit (PCU). PCU value for a vehicle varies with traffic and roadway condition around. A number of factors have been identified affecting PCU values. The current study aims at developing a fuzzy based model for the estimation of PCU value for trucks. Fuzzy based model is of importance because of a number of independent affecting factors. Results of developed fuzzy MATLAB based model are compared with the quoted results and are found with high degree of correlation.

The knowledge of roadway capacity is an important basic input required for planning, analysis and operation of roadway systems. Expressing capacity as number of vehicles passing a given section of road or traffic lane per unit time will be inappropriate when several types of vehicles with widely varying static and dynamic characteristics are comprised in the traffic. The problem of measuring volume of such heterogeneous taffic has been addressed by converting the different types of vehicles into equivalent passenger cars and expressing the volume in terms of Passenger

Car Unit (PCU) per hour. The effect of variation of traffic volume, road width, magnitude of upgrade and its length on PCU value is studied. A recently developed heterogeneous traffic-flow simulation model, named, HETEROSIM is used for this study. Field data collected on traffic flow characteristics are used in calibration and validation of the simulation model. The validated simulation model is then used to derive PCU values for different types of vehicles. The PCU estimates, made through microscopic simulation, for the different types of vehicles of heterogeneous traffic, for a wide range of traffic volume and roadway conditions indicate that the PCU value of a vehicle significantly changes with change in traffic volume, width of roadway, magnitude of upgrade and its length. Using the derived PCU values, capacity guidelines are also developed for typical roadway and traffic conditions.

Study on passenger car units for heterogeneous traffic and the use of a modified density method.The project team collected speed, flow and lateral placement data at 34 rural and suburban highway sites throughout India. These sites comprised six highway types. The study also rendered passenger car units for each Indian traffic type in relation to an Indian passenger car.

Paper summarising the results of the speed model calibration work

Calibration of HDM-4 speed model to India

Calibrating the free speed and congestion speed model to Thailand

Software used to calculate the used power of vehicles. Important for predicting speeds.

Memo presenting issues with capacity modelling in HDM-4's RUE model.

Paper by Karl Bang on the effect of side friction on speeds, with particular emphasis on the Indonesian Highway Capacity Manual study.

Paper by John Hine evaluating the traffic and fuel consumption in Indonesia, particularly with regard to HDM-4's proposed modelling approach.

Chris Bennett's PhD dissertation on developing a model for predicting speeds on two-lane highways in New Zealand

Paper describing how to undertake an economic analysis with congested traffic. Approach was adapted and enhanced for HDM-4

How congestion effects were modelled in HDM-95.

How traffic congestion was modelled in Bangladesh

Chris Bennett's PhD dissertation on developing a model for predicting speeds on two-lane highways.

Arrb Conference. The implications of speed measurement errors in speed surveys

The HDM-III report by the World Bank describing speeds and VOCs

Analysis of speeds on rural two lane highways in New Zealand.