Neda Masoud

A decomposition Algorithm to solve the multi-hop peer-to-peer ride-matching problem

In this paper, we mathematically model the multi-hop Peer-to-Peer (P2P) ride-matching problem as a binary program. We formulate this problem as a many-to-many problem in which a rider can travel my transferring between multiple drivers, and a driver can carry multiple riders. We propose a pre-processing procedure to reduce the size of the problem, and devise a decomposition algorithm to solve the original ride-matching problem to optimality by means of solving multiple smaller problems. We conduct extensive numerical experiments to demonstrate the computational efficiency of the proposed algorithm and show its practical applicability to reasonably-sized dynamic ride-matching contexts. Finally, in the interest of even lower solution times, we propose heuristic solution methods, and investigate the trade-offs between solution time and accuracy.

Car2work: Shared Mobility Concept to Connect Commuters with Workplaces

Over the past decade there has been a surge of shared-use mobility concepts that are redefining how people move in urban areas. In this context, a new shared-use mobility concept, Car2work, that fills the gap between the existing approaches by integration of those approaches with the transit network is proposed. Car2work differs from the traditional dynamic ridesharing approaches in the following ways: (a) it is designed for recurring trips; (b) the concept of drivers is dropped; instead, vehicles that carry at least one commuter are used; (c) commuters announce their trips in advance; and (d) multiple trips per commuter are allowed during the day. The main goal is to connect commuters with workplaces but to guarantee a trip home and offer some degree of flexibility. The proposed shared mobility system is modeled as a pure binary problem that is solved with an exact solution method. The solution method decomposes the original problem into a master problem and a subproblem, aggregating over the vehicles and reducing the number of decision variables and constraints. A link reduction strategy based on spatiotemporal constraints is also implemented to reduce the number of decision variables. Numerical experiments were performed for two scenarios. The first scenario included 10 commuters with two trips each, three workplaces, two transit stations, and 15 transfer points. The second scenario comprised 25 commuters with two trips each, four workplaces, two transit stations, and 31 transfer points. It is demonstrated that consideration of the transit network increases the matching rate and reduces vehicle costs.

Design for optimal quality by recycling returned products

For many manufacturers, the cost of replacing returned products may more than offset the cost of producing parts with a higher quality. This is especially true if good parts from returned products are used to remanufacture aftermarket products. Furthermore, such policy allows for satisfying a customer population with a variable expectation for acceptable quality. In this study, the total cost of supplying a given demand is derived as an analytical function of the targeted primary production rate and product quality when the demand is satisfied with a combination of primary and remanufactured products. The decision variables of this function consist of the primary production rate and the designed product and production quality. This cost is then minimised to determine the production rate and the optimum quality to target. A numerical example is provided and used to demonstrate the application of this function. This example also demonstrates the use of the proposed solution for optimising quality when there is a limit on primary production size. The example also demonstrates the use of the function for optimising service levels. The results show a close match between the theoretical functions developed in this study and those obtained from a Monte Carlo simulation model.

Promoting Peer-to-Peer Ridesharing Services as Transit System Feeders

Peer-to-peer (P2P) ridesharing is a recently emerging travel alternative that can help accommodate the growth in urban travel demand and at the same time alleviate problems such as excessive vehicular emissions. Prior ridesharing projects suggest that the demand for ridesharing is usually shifted from transit, but its true benefits are realized when the demand shifts from single-occupancy vehicles. This study investigated the potential of shifting demand from private autos to transit by providing a general modeling framework that found routes for private vehicle users that were a combination of P2P ridesharing and transit. The Los Angeles Metro Red Line in California was considered for a case study because it has recently shown declining ridership trends. For successful implementation of a ridesharing system, strategically selecting locations for individuals to get on and off the rideshare vehicles is crucial, along with an appropriate pricing structure for the rides. The study conducted a parametric analysis of the application of real-time P2P ridesharing to feed the Los Angeles Metro Red Line with simulated demand. A mobile application with an innovative ride-matching algorithm was developed as a decision support tool that suggested transit-rideshare and rideshare routes.

Peer-to-peer ridesharing with ride-back on high-occupancy-vehicle lanes: Toward a practical alternative mode for daily commuting

This paper presents a matching and pricing mechanism for a peer-to-peer ridesharing system that ensures a ride-back for matched riders. This service is thus presented as an alternative to driving alone for daily commuting. The matching algorithm is formulated as a minimum-cost, maximum-flow problem that is exact and quickly solvable on polynomial time. The mechanism modeling is based on the Vickrey–Clarke–Groves (VCG) mechanism that is known to be efficient, incentive compatible, and individually rational. However, VCG runs on a budget deficit in a ridesharing setting. To address this issue, participants were classified into drivers and riders in accordance with a novel multiparameter reserve price that fixes the revenue shortage problem and makes the system financially self-sustainable but in detriment of no longer being efficient. Agents’ utility functions include cost-sharing savings and high-occupancy-vehicle (HOV) travel time savings. The parametric study uses origin–destination demand data from the Southern California Association of Governments, and travel times are extracted from a professional web mapping service. Results show the method has a revenue surplus over most of the reserve-price parameter space and offers high matching rates attributable to the inclusion of HOV travel time savings and reserve-price structure. The reserve prices are drawn from empirical distributions of value of time and unit distance cost.

Designing a Transit-Feeder System using Multiple Sustainable Modes: Peer-to-Peer (P2P) Ridesharing, Bike Sharing, and Walking

Peer-to-peer (P2P) ridesharing is a relatively new concept that aims to provide a sustainable method for transportation in urban areas. Previous studies have demonstrated that a system that incorporates both P2P ridesharing and transit would enhance mobility. We develop schemes to provide travel alternatives, routes and information across multiple modes, which includes P2P ridesharing, transit, city bike-sharing and walking, within the network. This study includes a case study of the operation of the multimodal system that includes P2P ridesharing participants (both drivers and riders), the Los Angeles Metro Red line subway rail, and the Los Angeles downtown bike-share system. The study conducts a simulation, enhanced by an optimization layer, of providing travel alternatives to passengers during morning peak hours. The results indicate that a multi-modal network expands the coverage of public transit, and that ride- and bike-sharing could be effective transit feeders when properly designed and integrated into the transit system.

Classification of motor vehicle crash injury severity: A hybrid approach for imbalanced data

This study aims to classify the injury severity in motor-vehicle crashes with both high accuracy and sensitivity rates. The dataset used in this study contains 297,113 vehicle crashes, obtained from the Michigan Traffic Crash Facts (MTCF) dataset, from 2016-2017. Similar to any other crash dataset, different accident severity classes are not equally represented in MTCF. To account for the imbalanced classes, several techniques have been used, including under-sampling and over-sampling. Using five classification learning models (i.e., Logistic regression, Decision tree, Neural network, Gradient boosting model, and Naïve Bayes classifier), we classify the levels of injury severity and attempt to improve the classification performance by two training-testing methods including Bootstrap aggregation (or bagging) and majority voting. Furthermore, due to the imbalance present in the dataset, we use the geometric mean (G-mean) to evaluate the classification performance. We show that the classification performance is the highest when bagging is used with decision trees, with over-sampling treatment for imbalanced data. The effect of treatments for the imbalanced data is maximized when under-sampling is combined with bagging. In addition to the original five classes of injury severity in the MTCF dataset, we consider two additional classification problems, one with two classes and the other with three classes, to (1) investigate the impact of the number of classes on the performance of classification models, and (2) enable comparing our results with the literature.

Goal Programming Approach to Allocate Freight Analysis Framework Mode Flow Data

Several methods have been proposed to disaggregate Freight Analysis Framework (FAF) commodity flows to zonal structures of greater geographical detail. This disaggregation is usually performed on the basis of explanatory variables related to the supply and demand of goods. This paper studied a complementary procedure to determine the mode splits of disaggregated FAF flows. A goal programming approach was proposed to allocate FAF mode flow data on the basis of mode-related variables. The formulated goal programming problem minimized the deviation between the mode flow decision variables and target mode flow values, subject to given FAF mode flow information. The use of mode split models was proposed to define the problems target values. In a sample application of the procedure, a method to estimate aggregate mode split models with FAF data was discussed. Mode split models could be used by transportation organizations that did not have access to freight mode choice models to define the goal programming problems target mode flow values. In addition, an optimization problem was formulated to account for FAF mode flow data in the disaggregation of total commodity flows. Last, validation procedures for FAF disaggregation and mode allocation results were discussed, and an example of a validation approach was presented.