Route Guidance Map for Emergency Evacuation

An efficient process of emergency evacuation must be guided. In the event of an evacuation instruction, a significant amount of time is spent by evacuees looking for a place of relative safety or an exit. Due to the ensuing stress and confusion evacuees try to follow others, consequently, all the exits are not used effectively. Therefore, it is important to develop a route guidance map for the emergency. The focus of the map is to help both, the evacuees and the authorities to perform evacuation efficiently. This paper presents a route guidance map for pedestrians that aims an efficient evacuation in case of an emergency. An agent-based simulation framework is used for the simulation of various scenarios to prepare the guiding map. A real world case study of Sarojini Nagar, Delhi is presented to test the presented methodology. Eventually, several strategic recommendations are provided for improving safety of existing infrastructure

Improvement in direct bus services through route planning

Improvement in directness of bus services, i.e., without intermediate transfers, can be a key leverage to incentivize the use of bus transport across cities worldwide. Thus, there is a clear need to design the bus route network for an increase in direct bus services and consequently enhance bus transport attractiveness. Accordingly, this work develops two mathematical formulations to optimize route network redesign without a major overhaul of the existing network. The results of the formulations are easy to implement and also computationally tractable. The first formulation determines alternative feasible paths for all given routes. The second determines the set of paths that maximizes direct passenger-kilometers (PKM) travelled over the given road network specified by a set of Origin-Destination pairs. The approach is applied to an area in Delhi, India using CPLEX and MATLAB for computations and analyses. Results reveal that for the redesigned network, the improvement in direct PKM travel is more than 35%. Additionally, there is more than 8% travel time saving as compared to the existing network due to better bus stop coverage. Lastly, the formulations have the capability to either incorporate or leave out metro (or other public transport) services available in the city. Further, the models also give the ability to policy makers to set resource constraints according to the availability of resources

Mitigation of overcrowding in buses through bus planning

Mathematical models have been developed to address overcrowding in buses to incentivize their use. The models capture real-life requirements of bus planning, are computationally tractable, and easy to understand by decision makers. First, the current level of bus services on the given network is assessed. Then the models are developed to allocate the existing buses optimally and determine the minimum number of buses needed to satisfy the existing and future demand. Our results demonstrate that significant benefits can be obtained by the use of these models. The models also incorporate decision-making flexibility by allowing policy makers to adjust the policy parameters according to their requirements. As a result, they can be useful decision-making tools for city transport anywhere in the world, especially Delhi and other cities with similar problems

Comparing public bus transport service attributes in Delhi and Mumbai: Policy implications for improving bus services in Delhi

Delhi and Mumbai are large cities with a population of more than 15 million each. Both cities have large bus systems. However, the share of bus users is much higher in Mumbai as compared to Delhi. There is a need to enhance the share of bus users in Delhi to reduce the growing traffic externalities in the city. The objective of this work is to compare the existing bus services of Delhi and Mumbai, and identify feasible strategies to improve the bus system in Delhi. A total of 1347 and 1045 bus passengers were surveyed in Delhi and Mumbai respectively to compare the existing bus systems of both cities. Further, 14 transportation experts in Delhi were surveyed to identify feasible improvement strategies in Delhi. Multinomial Logit (MNL) model and Sign test were used to analyze the perception of bus users. The analyses reveal that overcrowding is a major concern in both the cities. But, unlike Mumbai, Delhi buses are perceived as less punctual and more time consuming by non-captive1 users. As a result, share of non-captive users that travel by buses in Mumbai is higher as compared to Delhi. In terms of interventions, improved comfort, punctuality, and safety are considered relatively easier to achieve in Delhi as compared to reduction in travel time by experts. It is concluded that strategies to improve comfort, punctuality, travel time, and safety should be prioritized for bus services in Delhi

Frequency Optimization Models for Reducing Overcrowding Discomfort

One of the most important aspects of improving public bus transport attractiveness is reducing overcrowding in buses. However, most of the mathematical models that focus on designing bus services minimize the total social cost without considering the overcrowding discomfort. Further, they are mostly non-linear in nature and use heuristic and meta-heuristic approaches. Thus, they are difficult to understand and use by practitioners. This work addresses these gaps through models that include overcrowding discomfort and are also easy to implement and contextualize by practitioners. The authors develop one non-linear and two linear models to determine the optimum frequency of buses and apply them over a network of 34 routes of Delhi. The results reveal that the existing number of buses in Delhi is not sufficient to cater to the existing travel demand of peak hours, even after their optimum allocation. The authors also present a step-by-step procedure to enable practitioners to determine the minimum additional number of buses required to reduce the target discomfort and waiting time

ABSTRACT FUNCTION-APPROXIMATION-BASED IMPORTANCE SAMPLING FOR PRICING AMERICAN OPTIONS

Monte Carlo simulation techniques that use function approximations have been successfully applied to approximately price multi-dimensional American options. However, for many pricing problems the time required to get accurate estimates can still be prohibitive, and this motivates the development of variance reduction techniques. In this paper, we describe a zero-variance importance sampling measure forAmerican options. We then discuss how function approximation may be used to approximately learn this measure; we test this idea in simple examples. We also note that the zero-variance measure is fundamentally connected to a duality result for American options. While our methodology is geared towards developing an estimate of an accurate lower bound for the option price, we observe that importance sampling also reduces variance in estimating the upper bound that follows from the duality.