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Abstract The paper presents an analysis of roadway factors and posted speed limits that affect theoperating speed at multi-lane highways in Egypt. Field data on multi-lane highways in Egypt areused in this investigation. The analysis considers two categories of highways. The first consists oftwo desert roads (Cairo–Alexandria and Cairo–Ismailia desert roads) and the second consists oftwo agricultural roads (Cairo–Alexandria and Tanta–Damietta agricultural roads). The paperincludes three separate relevant analyses. The first analysis uses the regression models to investigatethe relationships between operating speed (V85) as dependent variable, and roadway factors andposted speed as independent variables. The road factors are lane width, shoulder width, pavementwidth, median width, number of lanes in each direction, and existence of side access along each sec-tion. The second analysis uses the Artificial Neural Network (ANN) to explore the previous rela-tionships while the third one examines the suitability of the posted speed limits on the roadsunder study. It is found that the ANN modeling gives the best model for predicting the operatingspeed and the most influential variables on V85 are the pavement width, followed by the medianwidth and the existence of side access along section. 53198
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It is also found that the posted speed limithas a very small effect on the operating speed due to the bad behavior of drivers in Egypt. Theseresults are so important for controlling V85 on multi-lane rural highways in Egypt.ª 2012 Cairo University. Production and hosting by Elsevier B.V. All rights reserved. IntroductionHighway geometry and traffic speed consider the most impor-tant factors affecting the efficiency and safety of highway systems. Improving the geometry of multi-lane rural highwaysshould be a high priority for highway authorities, as thisrepresents an important component of the rural network.Traffic speed is an important parameter because it relates tosafety, time, comfort, convenience, and economics. The abilityto predict accurate vehicular operating speeds is useful forevaluating the planning, design, traffic operations, and safetyof roadways.In the present paper a driver’s speed under free flow condi-tions avoid the effect of traffic flow on vehicle speed, as onlythe effect of highway geometry and posted speed on operatingspeed is considered as stated by Hashim [1]. Geometric features that are considered important in affecting traffic speedare lane width, right shoulder width, number of lanes, medianwidth, existence of side access, and pavement width. These fea-tures will be considered beyond the scope here.Therefore, the first part in the analysis presented in this pa-per involves an investigation of speed–roadway relationshipusing linear regression models in order to predict operatingspeed under free flow condition on rural multi-lane highwaysin Egypt.As the Artificial Neural Networks (ANNs) is a new tech-nique which used all over the world for predicting purposes,it is necessary to assign this methodology to predict theoperating speed. However, the modeling of operating speed–roadway relationship by ANN models is another aspect of thispaper.Speed limits are used in most countries to regulate the speedof road vehicles. Speed limits are important to reduce the dif-ferences in vehicle speeds by drivers using the same road at thesame time which increases safety. Studying the impact of theposted speed limit on V85 for the roads under study is anotherobjective of this paper. This is performed by entering theposted speed limits in the regression and ANN models.According to the objectives of this paper, which are statedearlier, a detailed statistical analysis are carried out to examinethe speed characteristics on the selected field sites.
More specifically, the analysis is carried out for the follow-ing objectives: To investigate speed–roadway relationship correspondingusing conventional regression models and ANN models. To achieve the best relationship for safely road geometricdesign in future. To examine the suitability of the posted speed limit and thecompliance of the driver with it.Background studiesA free moving vehicle is a vehicle that is free from interactionwith other vehicles in the traffic stream; as only the effect ofhighway geometry on vehicle speed is considered. Severalauthors had various definitions of the case of free flow condi-tions. Homburger et al. [2], in the Fundamentals of TrafficEngineering, recommended 4 s as a minimum headway be-tween the following vehicle and the vehicle traveling aheadto define free flow, although larger values are preferred if traf-fic conditions permit. Poe and Mason [3] concluded that vehi-cles with headway equal to or greater than 5 s are consideredto be under free flow conditions. A free-flowing vehicle was de-fined by Fitzpatrick et al. [4] as having 5 s headway. Lammet al. [5] reported that the speed data is considered under flowconditions when the isolated vehicles have a time gap of atleast 6 s or heading a platoon of vehicles.Ali et al. [6] studied the interrelationship between the free-flow speed, posted speed limit, and geometric design variablesalong 35 four-lane urban streets in Fairfax County, Virginia.The models had R2=0.87 and 0.86, respectively. Correlationanalysis showed that posted speed, median width, and segmentlength had a significant effect on free-flow speed on urbanstreets. The coefficients of the previous variables were +2.1,+3.6, and +13, respectively. This indicated that a positivecorrelation between these variables and V85 was achieved.Figueroa and Tarko [7] studied the relationship betweenvarious roadway and roadside design features and operatingspeeds on four-lane roadways in Indiana. A regression modelwas used to estimate operating speed. The model for four-lanehighways had R2= 0.86. The model showed that increasingthe posted speed limit resulted in higher operating speeds. Italso showed that speeds are higher in rural areas. The coeffi-cients of the effective variables were +4.75, and +2.04,respectively. Therefore, there was a positive relationship be-tween these variables and V85.Fitzpatrick et al. [8] explored speed relationships andagency practices related to speed. The research team modeledoperating speeds at 78 suburban/urban sites in Arkansas,Missouri, Tennessee, Oregon, Massachusetts, and Texas. Onlythe posted speed limit was found to be a statistically significantpredictor of 85th percentile operating speed on urban–subur-ban arterials. The estimated models had R2= 0.90. The coef-ficient of posted speed limit was +0.98 which indicating apositive relation with V85.Wang et al. [9] studied the effects of cross section character-istics and adjacent land use on operating speeds in Atlanta,Georgia. Speed data were collected using 200 vehiclesequipped with GPS devices. A mixed model approach wasused to predict 85th and 95th percentile speeds for urbanstreets. The models had R2= 0.88 and 0.85, respectively. Itwas found that the number of lanes, presence of curb,and commercial and residential land uses were positivelyassociated with operating speed. For the V85 model, the coef-ficients of variables were +6.49, +3.01, +3.31, and +3.27,respectively.Himes and Donnell [10] investigated the effects of roadwaygeometric design features and traffic flow on operating speedcharacteristics along rural and urban four-lane highways inPennsylvania and North Carolina. A simultaneous equationsframework was used to model the speed distribution. Thissimultaneous equation modeling framework was first intro-duced by Shankar and Mannering [11] to model speeds on afreeway segment in Washington State. It was later exploredin depth and compared to limited information (e.g. OLSregression) and full-information (e.g. seemingly unrelatedregression) modeling methods by Porter [12]. They found thatdifferent geometric design features were associated with meanspeed and speed deviation in the left- and right-lanes suchas pavement, median width, and right shoulder width. Thecoefficients of the previous variables were +1.81, +2.23,and +7.44, respectively. Thus, there were strong positiverelationships between these variables and operating speed.Singh et al. [13] developed ANN models to predict V85 oftwo-lane rural highways in Oklahoma. Several input parame-ters, namely, roadway characteristics, traffic conditions, andaccident experience were considered in developing the ANNmodels. Data from a total of 241 two-lane rural highway siteswere collected and used in developing the ANN models. Fourmodels were developed. Model 1 includes Posted Speed butdoes not include Accident Data; Model 2 includes neitherPosted Speed nor Accident Data; Model 3 includes bothPosted Speed and Accident Data; and Model 4 does not in-clude Posted Speed but includes Accident Data. The modelshad R2= 0.93, 0.55, 0.95 and 0.74, respectively. It was con-clude that the developed ANN models were expected to be use-ful for prediction of V85 when roadway characteristics withposted speed limits change.