Economic impact of non-motorized transportation in Indian cities

Lack of a clear understanding regarding the economic impacts of non-motorized modes is a major reason why they are excluded from the transportation development agenda of cities in India. Keeping this aspect in mind the present study has been divided in to two parts. The first part tries to understand the non-motorized traffic evolution in India. It focuses on the declination of non-motorized modes, necessity to revamp it, the favorable conditions to promote them in India and the relative problems associated with it. It is found here that there is a necessity for defining the role of non-motorized modes in India for the viable implementation of infrastructure and policies related with it.     

A porous flow approach to modeling heterogeneous traffic in disordered systems

A continuum model that describes a disordered, heterogeneous traffic stream is presented. Such systems are widely prevalent in developing countries where classical traffic models cannot be readily applied. The characteristics of such systems are unique since drivers of smaller vehicles exploit their maneuverability to move ahead through lateral gaps at lower speeds. At higher speeds, larger vehicles press their advantage of greater motive power. The traffic stream at the microscopic level is disordered and defines a porous medium. Each vehicle is considered to move through a series of pores defined by other vehicles. A speed-density relationship that explicitly considers the pore space distribution is presented. This captures the considerable dynamics between vehicle classes that are overlooked when all classes are converted to a reference class (usually Passenger Car Equivalents) as is traditionally done. Using a finite difference approximation scheme, traffic evolution for a two-class traffic stream is shown.     

Analysing queueing at toll plazas using a coupled,multiple-queue,queueing system model: application to toll plaza design

A vehicle approaching a toll plaza observes the queues at each of the available toll-lanes before choosing which to join. This choice process, the arrival process of vehicles and the service characteristics of the toll-booths, affect the queues and delay the drivers. In this paper, queueing at a toll plaza is modelled as a multiple-queue queueing system where the arrival process to a queue (toll-lane) is dependent on the state of all the queues. In the past, such systems have been modelled mathematically only for two queues and are not applicable for toll plazas with three or more toll-lanes. The proposed model determines the steady-state probability density function (pdf) for the queues at large toll plazas. This study is used to determine the number of toll-lanes or the length of the upstream queueing area required to achieve certain user-specified levels-of-service. Expected delay and maximum queue length are used as level-of-service measures. Indicative design charts are also provided.     

Hot lane policies and their implications

This research examined the major changes in a corridor due to high occupancy/toll (HOT) lane implementation. This was accomplished by comparing the impacts of HOT lanes on three pairs of HOT lanes with similar design and operational characteristics. These pairwise comparisons of similar HOT lanes reduced the impact of exogenous factors and removed the issue of comparing HOT lanes that were so dissimilar it would be impossible to isolate the reasons for difference in results from the lanes. With strict registration requirements for free high occupancy vehicle (HOV) 3+ travel on the I-95 Express Lanes (ELs) in Miami there were indications that some carpoolers switched to lower occupancy modes. Tolled access for HOV2s on I-95 and the SR-91 ELs near Los Angeles resulted in lower usage of those ELs by the HOV2s as compared to most HOV lanes where HOV2 access is free. On the SR167 (Seattle) and I-25 (Denver) HOT lanes, exogenous factors like the price of gas and the economic recession seemed to be the primary influence on the usage of those HOT lanes. In both cases, carpool usage increased along with the price of gas. On I-25, the increasing unemployment rate coincided with a decrease in toll paying travelers. On SR 167 there were also indications of mode shifts among the transit, carpool and toll paying SOVs due to the fluctuating price of gas.     

Congestion avoidance in city traffic

The number of vehicles on the road (worldwide) is constantly increasing, causing traffic jams and congestion especially in city traffic. Anticipatory vehicle routing techniques have thus far been applied to fairly small networked traffic scenarios and uniform traffic. We note here a number of limitations of these techniques and present a routing strategy on the assumption of a city map that has a large number of nodes and connectivity and where the vehicles possess highly varying speed capabilities. A scenario of operation with such characteristics has not previously been sufficiently studied in the literature. Frequent short‐term planning is preferred as compared with infrequent planning of the complete map. Experimental results show an efficiency boost when single‐lane overtaking is allowed, traffic signals are accounted for and every vehicle prefers to avoid high traffic density on a road by taking an alternative route. Comparisons with optimistic routing, pessimistic routing and time message channel routing are given. Copyright © 2014 John Wiley & Sons, Ltd.     

A new approach for deriving temperature and salinity fields in the Indian Ocean using artificial neural networks

This work reports a new methodology for deriving monthly averages of temperature (T) and salinity (S) fields for the Indian Ocean based on the use of an artificial neural network (ANN). Investigation and analysis were performed for this region with two distinct datasets: (1) monthly climatological data for T and S fields (in 1° × 1° grid boxes) at standard depth levels of the World Ocean Atlas 1994 (WOA94), and; (2) heterogeneous randomly distributed in situ ARGO, ocean station data (OSD) and profiling (PFL) floats. A further numerical experiment was conducted with these two distinct datasets to train the neural network model. Nonlinear regression mapping utilizing a multilayer perceptron (MLP) is employed to tackle nonlinearity in the data. This study reveals that a feed-forward type of network with a resilient backpropagation algorithm is best suited for deriving T and S fields; this is demonstrated by independently using WOA94 and in situ data, which thus tests the robustness of the ANN model. The suppleness of the T and S fields derived from the ANN model provides the freedom to generate a new grid at any desired level with a high degree of accuracy. Comprehensive training, testing and validation exercises were performed to demonstrate the robustness of the model and the consistency of the derived fields. The study points out that the parameters derived from the ANN model using scattered, inhomogeneous in situ data show very good agreement with state-of-the-art WOA climatological data. Using this approach, improvements in ocean climatology can be expected to occur in a synergistic manner with in situ observations. Our investigation of the Indian Ocean reveals that this approach can be extended to model global oceans.