From the literature mentioned above it is observed that only few attempts have been made to calibrate VISSIM for Indianheterogeneousconditions. Also several studies do not perform sensitivity analysis to find the parameters which affect VISSM models and then calibrate only those parameters. In this study sensitivity analysis is done by both ANOVA method and optimized trajectories for elementary effect. Also calibration is automated with the help of Genetic Algorithm tool of MATLAB through COM interface of VISSIM. Model is validated with a different data set. A second level of sensitivity is also done to find second order sensitive parameters from among the parameters which were found to be not sensitive in the first round of sensitivity analysis. This may be because few parameters may dominate the sensitivity of the model and once these are fixed, other parameters may show up as significant. The network is again calibrated with one hour data taking into account the second order sensitive parameters. Validation is done for next one hour data set from same intersection.
Microscopic traffic simulation is a rapidly developing field and many studies have been done to more clearly define the abilities and limitations of the models. The majority of studies done focus on parameter optimization as opposed to the parameter sensitivity analysis as done for this project. Many utilize a genetic algorithm in order to create the best set of input parameters so that the model produces results similar to reality. The studies that focus on VISSIM tended to be divided between those examining freeway and those examining signalized intersection operations. The freeway studies generally use the Wiedemann 99 car following parameters, and the urban studies use the Wiedemann 74 parameters. In all of the studies these parameters are considered to be important to the model’s calibration and results. Both groups of studies also typically include parameters such as look-ahead distance and waiting time before diffusion in addition the car following parameters. Outlined below are many of the studies done to investigate the effects or optimization of the input parameters in VISSIM.
Transportation and its environmental impact, as well as mobility, are two of the most important factors considered in urban economy and quality of life . Hence traffic simulation is crucial for transportation and road planning . Ca- libration of certain parameters is needed to evaluate traffic and planning opera- tions and applications, being critical for obtaining realistic microsimulation re- sults . Calibration is essential to obtain reliable results, along with the appro- priate data collecting technique, which mostly depends on cost and access . Unmanned Aerial Vehicles (UAV or drones) images represent a low cost and accessible method to capture vehicle operations over time . Several techniques have been used to improve calibration in VISSIM. Occasionally, the parameter configuration (such as average standstill distance and additive part of safety dis- tance) should be determined for one location, due to the area’s distinct characte- ristics. Without proper calibration, the simulated traffic outcome does not coin- cide with real-world settings, and microsimulation models cannot help analysts to solve any traffic issue .
Microscopic simulation models need to be calibrated to represent realistic local traffic con- ditions. Traditional calibration methods are conducted by searching for the model param- eter set that minimizes the discrepancies of certain macroscopic metrics between simulation results and field observations. However, this process could easily lead to inap- propriate selection of calibration parameters and thus erroneous simulation results. This paper proposes a video-based approach to incorporate direct measurements of car- following parameters into the process of VISSIM model calibration. The proposed method applies automated video processing techniques to extract vehicle trajectory data and uti- lizes the trajectory data to determine values of certain car-following parameters in VISSIM. This paper first describes the calibration procedure step by step, and then applies the method to a case study of simulating traffic at a signalized intersection in VISSIM. From the field-collected video footage, trajectories of 1229 through-movement vehicles were extracted and analyzed to calibrate three car-following parameters regarding desired speed, desired acceleration, and safe following distance, respectively. The case study demonstrates the advantages and feasibility of the proposed approach.
Al-Corniche Street located in the downtown area of the city of Doha, Qatar was selected to collect field data for calibration purposes. This corridor has four roundabouts and one signalized intersection with three lanes in each direction at the speed limit of 80 km/h. The selected street consists of 4 segments with a length of 1.5, 0.85, 2, and 0.45 kilometers, respectively. Travel time between the segments and maximum queue length from multiple different approaches at the intersections were selected as measures of performance. Two vehicles equipped with GPS were used in each direction to record locations and times. Based on the relationship between time and space, travel times between the roundabouts and the intersection were compared from one to another. Data collection was performed from 6:00 AM to 9:00 AM, 11:00 AM to 2:00 PM and 5:00 PM to 8:00 PM on a regular weekday in May 2013.
that the simulation model response same with the measured field condition. Siddharth S.MP et. al,(2013) present a method for sensitive analysis and automatic calibration of VISSIM by using traffic data. ANOVA and elementary method are used for sensitive analysis. There are different studies in which many authors compare the performance of VISSIM software with another computer program and VISSIM is recognize as the best result among them. Arpan Mehar et. al,(2013) presented the applicability of simulation software VISSIM for determination of the capacity of highway under heterogeneoustraffic condition. And he observed that VISSIM overestimates the capacity and speed of highway in its original form. But after modification of driver behavior parameters CCO and CC1 the simulated capacity and field capacity almost same. Yadav Anamika et. al,(2014) estimates the capacity of
factors for different categories of vehicles under heterogeneoustrafficconditions prevailing on Indian roads using a microscopic simulation model, HETEROSIM. The PCU values for the different types of vehicles, at various volume levels, on four lane divided urban roads were estimated by taking the average stream speed as the measure of performance. In this study it was found that for the traffic composition considered, the PCU value of all categories of vehicles follows an increasing trend from low volume level of about 500 vehicles per hour to high volume level of about 2000 vehicles per hour, and then decreases at higher volume level of about 4000 vehicles per hour. Geistefeldt (2009) developed a new method for estimating passenger car equivalents for heavy vehicles on freeways. The proposed approach was based on the concept of stochastic capacities. Capacity-distribution function was estimated in passenger car units. Passenger Car Equivalent was determined for which the variance of the capacity distribution function becomes minimal. The suggested PCEs tend to decrease with an increasing number of lanes. The highest PCE was estimated for an uphill section. Arasan and Arkatkar (2010) studied the variation of PCU values for a wide range of traffic volume and roadway conditions for different categories of vehicles under heterogeneoustrafficconditions prevailing on Indian intercity roads using microscopic simulation. It was found that in the case of vehicles that are larger than passenger cars, at low volume levels, the PCU value decreases with increase in traffic volume and at high traffic volume levels, the PCU value increases with increase in traffic volume. Whereas, in the case of vehicles that are smaller than passenger cars, at low volume levels, the PCU value increases with increase in traffic volume and at high volume levels, the PCU value decreases with increase in traffic volume.
Abstract: Free-flow speed (FFS) is the desired speed that drivers choose when no (or very less number of) vehicles are present in the road segment. FFS is an important parameter of traffic flow that decides the level of service and capacity aspects of various types of highway facilities. Estimation of FFS is extremely time consuming and requires extensive human resource and capital. Hence, a FFS model can be a solution to bring down the above difficulties while ensuring satisfactory prediction of FFS. In countries like India, a widely used method of estimating FFS is to collect vehicle speeds from field during low volume hours. However, this method requires significant amount of time, human resource and capital for studies on large road networks. Hence, it is essential to develop models to predict free-flow speeds. It is important that models are capable of capturing the free-flow speed variations due to local road factors. Majority of the existing free-flow speed models are developed under homogeneous trafficconditions, in which passenger cars dominate the vehicle composition. However, the traffic condition in emerging economies like India is heterogeneous in nature characterized by the presence of multiple vehicle categories with varying physical and dynamic characteristics. The present paper attempts to investigate the influence of different road factors on FFS on urban roads of Chennai, India. The paper also tries to capture the FFS variations across different classes of vehicles and develop FFS prediction models. The typical Indiantraffic comprises significant percentage of slow moving vehicles like three-wheelers as well as fast moving sedans and SUVs. Composition of traffic and corresponding proportions of different classes are important factors that differentiate heterogeneous and homogeneous traffic. The presented models could explore the driver speed behavior with respect to the aforementioned factors into consideration.
The estimated PCE values, for all the considered vehicle categories are found to decrease with increase in their respective proportions. Robert  defined it as the ratio of number of cars removed to number of vehicles added. The impedance caused by vehicles for a chosen volume level was calculated by replacing a certain percentage of cars with respective types of vehicles. Chandra and Kumar  studied the effect of lane width on PCE values and also on the capacity of a two-lane road under mixed trafficconditions. “PCUs were estimated at ten road sections for nine categories of vehicles. They found that the PCE for a vehicle type increases linearly with the width of carriageway. Al-Kaisy et al.,  worked on developing passenger car equivalency factor for heavy vehicles during congestion. A set of PCE factors for oversaturated trafficconditions was developed for use in traffic analyses. Arkatkar  estimated the PCE values by studying the influence of roadway and traffic characteristics such as variation in traffic volume, road width and magnitude of upgrade and its length”. Manraj et al.,  used speed as performance measure and estimated PCE values for Indian expressways using simulation technique. They studied the effect of vehicle composition on PCE values and evaluated capacity of expressways and found that PCE decreases with increase in volume capacity ratio irrespective of vehicle category. They concluded that PCE values decreases for all categories, when their proportion increases in the traffic stream. It is found that due to the complex nature of interaction between vehicles under the heterogeneoustraffic condition, the PCE estimates made through simulation for different types of vehicles of heterogeneoustraffic, significantly changes with change in traffic volume level. Arpan
Abstract: Developing countries like India are climbing the ladder of development very fast. So in relation to the development there is rapid increase in traffic volume. Mainly traffic in developing country is heterogeneous nature that means it consist of vehicles that move with different speed, have different size, have different operating characteristics and vehicle spacing may also vary. There is acute need of an efficient and intelligent traffic system to deal with problems arising due to heterogeneous nature of traffic .Traditionally in India whatever design equations are used to design roads, considered the traffic nature as homogeneous but as said earlier Indiantrafficconditions are heterogeneous in nature .This paper reviews the mixed traffic in cities and finds out which factor need to be considered in such mixed trafficconditions. In this paper we considered the microscopic parameters such as speed, flow, density at a signalized intersection, which intern will help to develop new equations for heterogeneoustrafficconditions in Indian cities.
Speed portrays the traffic performance measure of the roads and highways. It gives the basic fundamental relationships of traffic flow theory. In the mixed traffic, speed of one type of vehicle is affected by other vehicles in the traffic stream. Hence, speed equations for individual sort of vehicles were developed using traffic composition and spot speed values. In the present study, PCU values are calculated by using the methodology proposed by Chandra. S [1, 2], which is best suitable for the Indiantrafficconditions.
Earlier traffic noise levels in a few Indian cities have been reported by previous researches [Pancholy, et al 1969, Bose yya 1973]. In recent years the studies reported are due to Gupta et al 1986, Rao and Rao, 1991, Chakrabarty 1997 etc. In these studies regression equations have been reported for predicting the traffic noise levels by considering ome studies (Kumar and Jain 1994, Agarwal et. al 2009 and Agarwal and swami 2010) also dealt with heterogeneoustrafficconditions and arrived at some correction values for mixed traffic flow. Though these studies have been reported about this traffic noise the applicability of these models in the present urban context is not straight forward. Especially the establishment of the base noise level which should also include predominant sources such as honking noise by vehicle horns may have to considered even while considering continuous flow. The work presented considers a procedure for predicting the noise levels of heterogeneoustrafficconditions by considering base
Urban travel demand is generated directly from a trip diary survey (TRIPP, iTrans, and VKS 2009). Table 1 shows the modal income statistics for households of Patna city. This data is evaluated from individual monthly income form trip diaries. 2 Car is predominantly used by high income persons whereas motorbike is used by mid to high income persons. Bicycle and walk trips are limited to low income households. Trip diaries result in 13,278 records which represent approximately 1% sample of all trips. Every such record is translated into one agent with one plan. In absence of other data, for each plan two trips are generated, one ‘to work/education/social/other’ and one ‘back home’. This is somewhat similar to generating an AM peak and a PM peak origin-destination-matrix. In order to get significant number of plans for commuters and through traffic in various categories (see Section 3.1.2 and Appendix A), the data is expanded to a 10% sample. Therefore, urban plans are cloned as follows:
real Internet data. In particular a huge effort has been produced to construct generators of graphs that reproduce the features actually measured in the Internet network (see for example [6,7]). Previous work that addresses the congestion problem in flow problems on graphs and more specifically the problem of designing routing sche- mes able to avoid congestion is, for example, the work contained in the papers of Aumann and Rabani , Oka- mura and Seymour , Leighton  and Leonardi . In  and  the network congestion problem is formu- lated as a problem concerning the max-cut ratio of a graph while in  and  the problem of finding frac- tional routing strategies with good congestion properties is addressed. The data packet traffic is described by the routing strategy and by the traffic management rules. The routing strategy considered here to move the packets from their origin to their destination is the shortest weighted path. The traffic management rules are the rules used to manage the data packet traffic at the nodes where more than one data packet is waiting to be directed. The traffic management rules considered in our study are a simple variation of the “first in first out” rule.
The performance of all the prior methods and our algorithm on heterogeneoustraffic datasets is shown in Table. 2. We compute the average displacement error and the final dis- placement error for all the instances and we also count the error for pedestrians, bicycles and vehicles, respectively. The social attention (SA) model considers the spatial relations of instances and has smaller error than RNN ED and Social LSTM. Our method without category layer (TP-NoCL) not only considers the interactions between instances but also distinguishes between instances by using different LSTMs. Its error is similar to SA. By adding the category layer with- out self attention, the prediction results of TP-NoSA are more accurate in terms of both metrics. The accuracy im- provement becomes is more evident after we use the self- attention mechanism in the design of category layer. Our al- gorithm, TrafficPredict, performs better in terms of all the metrics with about 20% improvement of accuracy. It means the category layer has learned the inbuilt movement patterns for traffic-agents of the same type and provides good guid- ance for prediction. The combination of the instance layer and the category layer makes our algorithm more applicable in heterogeneoustrafficconditions.
. The common characteristic of both methods is that a separate calibration is needed for every single measuring problem. The first method can be used only for primitive shapes (one- or two-coordinate), because the CMM is calibrated using a standard similar to the measuring object. The virtual CMM method is based on a computer simulation of a measurement and can also be used also for more complex shapes. Before the simulation it is necessary to establish geometrical errors of the CMM which are later used for predicting the error of measurement of an element with ideal shape and dimensions. The accuracy of this method directly depends on the accuracy of the measurement of geometric errors. If the measurement of geometric errors is not traceable, then the calibration method is also not traceable.
In Vissim zit de functie dynamische routekeuze. Bij deze functie rijdt het verkeer van een beginzone naar een eindzone. Het vertrekpunt en de bestemming is vastgelegd in een O-D matrix (Origin-Destination matrix). Bij dynamische routekeuze in Vissim wordt er gebruik gemaakt van “parkeerplaatsen”. Dit zijn de begin- en eindpunten van elke route. Een parkeerplaats kan ook op de rand van het netwerk liggen, voor ritten die zich voortzetten buiten het gesimuleerde netwerk. De routekeuze wordt gebaseerd op de “kosten” van een route. Dit geeft aan hoe aantrekkelijk een route is. De kosten bestaan uit een gewogen sommering van de reistijd, afstand en extra kosten op de link. De weegfactoren kunnen zelf ingesteld worden. In Vissim zullen niet alle voertuigen gebruik maken van de route met de laagste kosten, maar een betere route is wel aantrekkelijker. De inverse van de kostenfunctie is de nutsfunctie. Wanneer de kosten het laagst zijn, is het nut van deze route dus het hoogst. De nutsfunctie gecombineerd met het Logit model en het Kirchhoff model maken samen het algoritme van de routekeuze in Vissim. Op de formules wordt later in dit rapport verder ingegaan.
The APHP contract is tailored to reflect differences in riskiness across counties and among individual insureds within a county. Driscoll (1988, pp. 28-30) observed that there are three approaches to ratemaking. There are the pure premium approach, loss ratio approach and judgement. The basic ratemaking procedure used by the FCIC is an empirical application of the pure premium approach, which is similar to the experience rating suggested by Driscoll, but with several added adjustments that are based on analysis of underlying relationships for different crops and geographical location. Milliman & Robertson (M&R) have said, “ For long term efficiency, it is imperative that the procedure for determining rates be responsive to changing conditions and distribute costs equitably among producers. Failure to do so will result in rates not representative of current conditions and less than optimal levels of participation of low cost producers.”
In the direction of complex heterogeneous traﬃc modeling, the present study proposes a model to simulate the behavior of smaller vehicles in the congested regime called seepage action. As the name suggests, in congested part of links, smaller vehicles like motorbikes and bikes do not stop at the end of queue. Instead, they move continuously across the gaps between the stationary congested vehicles and come in front. This behavior is rarely modeled and quantiﬁed even though it is common praxis in most of the developing nations. In order to facilitate this behavior, a state of the art queue model is modiﬁed to allow for seepage in congested regime. Furthermore, the concept of backward traveling holes is introduced. Thus, the congested branch of the fundamental diagram is modeled more realistic.
This paper shows that the OD matrix may have a significant influence on the macroscopic traffic dynamics related to a given road network. In particular, the mean travel distance is even more sensitive to this factor than to internal trafficconditions. The MFD shape can also be modified when different flow distributions are applied to the same network. A refined modeling framework has been presented in this paper to represent the reservoir internal dynamics. The idea is to partition the paths within a reservoir into several macroscopic routes where traffic dynamics are separately tracked. The first advantage is that the reservoir perimeter is no longer uniform because several macroscopic origins and/or destinations are identified. The second advantage is that routes with significantly different travel distances can be distinguished. The resulting framework proposes a better description of wave propagation between the reservoir upstream and downstream frontiers compared to existing approaches. It also permits a clear distinction of the flow directions between different parts of the reservoir perimeter. Numerical results have been obtained using a Godunov scheme combined with the HLL Riemann approximate solver. This combination provides accurate results in terms of wave propagation while remaining fast and simple in terms of computational implementation.