Development of a driving simulator
An in-depth understanding of the nature of human driving is necessary to study traffic system dynamics, causes for accidents, and traffic congestion etc. and to propose effective solutions to mobility related problems. This understanding is usually acquired through driver behavioural studies that investigate specific aspects of driving. With the advent of high-end computing and virtual reality technologies, driving simulators are popularly being used for conducting such studies and have several advantages. For example, driving situations are controllable and reproducible, costs are lower, and there is no risk for subjects, all of which allow studying the effects of sleep deprivation, distraction, etc. on traffic. The simulator is an interactive system in which a ‘respondent’ drives an ‘animated vehicle’ in a computer-generated ‘traffic environment’. Although several driving simulators exist, little work has been done in generating the traffic environments that are close to real world driving behaviours. Moreover, most driving simulators are developed for evaluating the behaviours of only car drivers in lane-based environs. However, the traffic conditions in developing countries like India are characterised by multiple vehicle types (two-wheelers, auto-rickshaws, buses and trucks) and absence of lane discipline. Thus, the aim of this study is to develop a driving simulator capable of representing realistic traffic environs based on robust driver behaviour models and while handling multiple vehicle types and disordered traffic movements that is observed in Indian conditions.
Cab Allocation and Routing
Cab aggregators commonly face the issue of matching ride requests to vehicles in near real-time. In addition to cab-hailing services such as Ola and Uber, this problem is also relevant to operations of popular food-delivery services such as Zomato and Swiggy. This topic can be addressed optimally in offline settings at least when the number of customers and cabs are reasonably small using a variety of pre-processing algorithms and integer programming. However, in practice, such decisions are made online when drivers are searching for passengers or are in the process of serving one or more customers. In this project, we address the problem of developing allocation and routing strategies that are adaptative and utilize future anticipated demand. The problem is currently addressed by minimizing the supply of cabs required with a penalty for lost trips and will be expanded to model other objectives. Several visualization tools for understanding the performance of the proposed models are also being developed using the New York City taxi data set.
Development of a traffic modelling framework for analysis of strategies aimed at decongesting Electronic city
The objective of this project is to measure and model traffic patterns in the Electronic City Phase-1 transportation network for evaluation of policies and strategies aimed at traffic decongestion in the area. A traffic flow modelling framework for the Phase-1 network is being created using a well-calibrated microscopic traffic simulator (VISSIM). Data on traffic patterns required for the project include classified traffic volumes, travel times, etc. on various links in the network, and delays and turning traffic volumes at intersections. Traffic volume, delay, and speed data is currently being collected using a sophisticated traffic counting equipment (i.e., the Traffic Infra-Red Logger or the TIRTL) and advanced computer vision techniques. This data will be fused with the demand data from another ongoing project and using a traffic assignment tool, OD matrices for the entire area will be constructed. Using standard signal warrants and the Indian Highway Capacity Manual (Indo-HCM) signal designs will be proposed to ease congestion in the area. Further, one-way/two-way and dynamic lane-reversal strategies will also be suggested based on demand patterns and the outcomes will also help provide technical support for further changes to the supply side.
Travel behavior analysis of commuters to Electronics City
Located in south-east Bangalore, Electronics City is home to technical and manufacturing companies including national giants like Infosys, Wipro, and TCS, and multinationals like HP, GE, and 3M. Connected to the rest of Bangalore by the Electronics City flyover and Nice Road, this area is facing congestion due to growing traffic. To explore solutions to the traffic problems faced, CiSTUP researchers aim to model: a) the city traffic based on traffic counts and origin-destination matrices to obtain models that respond accurately to traffic management measures. b) the commuters’ travel behaviour to understand the factors that influence various trip choices, for example that of mode and route, made by the commuters to Electronics City. Information combined from the traffic and the travel behaviour models provide inputs to policies for commuter trips both within and outside of Electronics City. The origin-destination (OD) matrices obtained from the travel study are used to calibrate the traffic model which model the effects of different traffic management measures. The travel models are also used to ascertain the various factors that influence commuter’s choice of mode, the knowledge of which may be used to influence more sustainable choices. Travel information of the Electronics City commuters will be obtained through a web and app-based survey: the web survey poses questions on demographics and travel-related choices; the optional app-based survey, traces the routes of trips made and draws inferences on modes used for each trip using an open-source platform. Besides studying present travel choices, the travel study will also investigate future travel scenarios, for example those after completion of metro rail construction. Different last-mile connection possibilities will be explored under the realm of mobility as a service (MaaS). Finally, the need for Electronics City to develop its own transport service to supplement public transit and company-based shuttle services will be explored. This project is supported by Electronic City Industrial Township Authority (ELCITA).
Development of Transit Ridership Prediction Models
Ridership prediction models show the effect on ridership of various measures such as changes in routes, service frequency, schedules, bus fares, connectivity to and from other modes of travel, improvements in bus stop amenities, and provision of information to travellers. With a daily ridership close to 30 lakhs, BMTC manages a fleet of over 6500 buses. Its buses equipped with GPS and ETM produce large volumes of data on its ridership and route patterns that may be analyzed to provide useful information for the transit company. Similarly, KSRTC buses operating in Mysore have similar ITS features and generate large quantities of data. Bus stops in Mysore are also equipped with electronic boards that show real-time information on its buses. While its ridership is increasing overall, it is losing mode share. Initial analysis of the data from these transit companies was undertaken to observe variations in patterns over weekdays, weekends, public holidays, and during peak and off-peak hours of the day. Overall ridership variations are observed as well as those over a route and in a bus station. Currently schedules of buses are being analysed using the GPS data to understand the effect of reliability on ridership and to identify complementarity and competition between routes. Finally, direct demand models will be estimated for the ridership prediction tool.
Solutions to Bus Bunching Problems
Bus bunching which causes multiple buses on a single route to group together is a major issue faced by most transit agencies. Such situations occur naturally when there are fluctuations in the network travel times or in passenger demand. Bus bunching results in crowding of the lead buses and affects the level of service of transit operations. Over the recent past, certain types of holding strategies have been found to be effective to counter this phenomenon. Specifically, these models calculate the optimal amount of time to wait at selected locations to equalize headways among all the buses serving a route. Such techniques have the potential to balance passenger load and improve reliability and ridership. A similar headway-equalizing mechanism is being developed to tackle this issue on BMTC’s buses in Bangalore.