MAXIMISING PROFITS FROM PASSENGER TRANSPORT SERVICE USING TRANSPORTATION MODEL ALGORITHM

The diversity and complexity of the different types of passenger transportations in operation today invokes the need for an efficient transport service management system. Existing transportation models tend towards proffering solution for finding the least cost combination for delivering cargoes from various depots to known remote customer destinations. This paper looks at the possibility of adopting and or modifying the existing model for use in the management of passenger transport services. A preliminary investigation using the Nigerian private transport sector management practices situation show that inability to apply scientific based approach to vehicle capacity assignment and passenger volume projection stands in the way of profit maximization for most indigenous transport companies. The paper clearly suggests that adopting the transportation model algorithm for estimating the best vehicle assignment method to routes will optimize operational decisions

Synergistic Interactions of Dynamic Ridesharing and Battery Electric Vehicles Land Use, Transit, and Auto Pricing Policies

It is widely recognized that new vehicle and fuel technology is necessary, but not sufficient, to meet deep greenhouse gas (GHG) reductions goals for both the U.S. and the state of California. Demand management strategies (such as land use, transit, and auto pricing) are also needed to reduce passenger vehicle miles traveled (VMT) and related GHG emissions. In this study, the authors explore how demand management strategies may be combined with new vehicle technology (battery electric vehicles or BEVs) and services (dynamic ridesharing) to enhance VMT and GHG reductions. Owning a BEV or using a dynamic ridesharing service may be more feasible when distances to destinations are made shorter and alternative modes of travel are provided by demand management strategies. To examine potential markets, we use the San Francisco Bay Area activity based travel demand model to simulate business-as-usual, transit oriented development, and auto pricing policies with and without high, medium, and low dynamic ridesharing participation rates and BEV daily driving distance ranges. The results of this study suggest that dynamic ridesharing has the potential to significantly reduce VMT and related GHG emissions, which may be greater than land use and transit policies typically included in Sustainable Community Strategies (under California Senate Bill 375), if travelers are willing pay with both time and money to use the dynamic ridesharing system. However, in general, large synergistic effects between ridesharing and transit oriented development or auto pricing policies were not found in this study. The results of the BEV simulations suggest that TODs may increase the market for BEVs by less than 1% in the Bay Area and that auto pricing policies may increase the market by as much as 7%. However, it is possible that larger changes are possible over time in faster growing regions where development is currently at low density levels (for example, the Central Valley in California). The VMT Fee scenarios show larger increases in the potential market for BEV (as much as 7%). Future research should explore the factors associated with higher dynamic ridesharing and BEV use including individual attributes, characteristics of tours and trips, and time and cost benefits. In addition, the travel effects of dynamic ridesharing systems should be simulated explicitly, including auto ownership, mode choice, destination, and extra VMT to pick up a passenger

Keys to effective transit strategies for commuting

Commuting poses relevant challenges to cities\u2019 transport systems. Various studies have identified transit as a tool to enhance sustainability, efficiency and quality of the commute. The scope of this paper is to present strategies that increase public transport attractiveness and positively impact its modal share, looking at some case studies and underlining key success factors and possible elements of replica to be ultimately planned in some of the contexts of the Interreg project SMART-COMMUTING. The strategies analyzed in this paper concern prices and fares, service expansion, service improvements, usage of vehicle locators and other technology, changes to the built environment. Relevant gains in transit modal share are more easily achievable when considering integrations between various strategies, thus adapting and tailoring the planning process to the specific context

Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining

In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are required to fulfill the given amount of trip demand. For this purpose, first, we develop a deep neural network model, called ST-NN (Spatio-Temporal Neural Network), to predict taxi trip time from the raw GPS trip data. Secondly, we develop a carpooling simulation environment for RL training, with the output of ST-NN and using the NYC taxi trip dataset. In order to maximize transportation efficiency and minimize traffic congestion, we choose the effective distance covered by the driver on a carpool trip as the reward. Therefore, the more effective distance a driver achieves over a trip (i.e. to satisfy more trip demand) the higher the efficiency and the less will be the traffic congestion. We compared the performance of RL learned policy to a fixed policy (which always accepts carpool) as a baseline and obtained promising results that are interpretable and demonstrate the advantage of our RL approach. We also compare the performance of ST-NN to that of state-of-the-art travel time estimation methods and observe that ST-NN significantly improves the prediction performance and is more robust to outliers.Comment: Accepted at IEEE International Conference on Big Data 2018. arXiv admin note: text overlap with arXiv:1710.0435

Distribution of Value of Time and Ways to Model Value of Time in Long-Range Planning Models

As managed lanes (ML) become more integrated in regional urban networks with existing general purpose (GP) lanes, the distribution of travelers’ value of time (VOT) is becoming more important for transportation planning agencies to quantify in order to accurately predict future travel patterns. Since travelers’ VOT varies depending on a multitude of factors, this study investigates ways that we can determine the VOT distribution of a region from existing travel data as well as effective ways that we can model VOT using traffic assignment algorithms. In networks with available link volumes and toll data on segments where travelers have the option of choosing to stay on the GP lanes or entering a ML facility, a VOT distribution can be inferred assuming that travelers who enter the ML choose to do so based on a certain “threshold” VOT. When modeling these VOT distributions, errors are observed in the traffic assignment results when both the continuous nature of VOT distributions are discretized, and when varying toll values are assumed to be constant. Specifically in the context of TransCAD software, link travel time errors appear to be much less significant than flow errors when tested on a nine node network. Additional experimentation on larger regional networks is needed to verify the significance of these errors and their impact on predicted travel patterns.Civil, Architectural, and Environmental Engineerin

Arterial traffic signal optimization: a person-based approach

This paper presents a traffic responsive signal control system that optimizes signal settings based on minimization of person delay on arterials. The system's underlying mixed integer linear program minimizes person delay by explicitly accounting for the passenger occupancy of autos and transit vehicles. This way it can provide signal priority to transit vehicles in an efficient way even when they travel in conflicting directions. Furthermore, it recognizes the importance of schedule adherence for reliable transit operations and accounts for it by assigning an additional weighting factor on transit delays. This introduces another criterion for resolving the issue of assigning priority to conflicting transit routes. At the same time, the system maintains auto vehicle progression by introducing the appropriate delays for when interruptions of platoons occur. In addition to the fact that it utilizes readily available technologies to obtain the input for the optimization, the system's feasibility in real-world settings is enhanced by its low computation time. The proposed signal control system was tested on a segment of San Pablo Avenue arterial located in Berkeley, California. The findings have shown the system's capability to outperform static optimal signal settings and have demonstrated its success in reducing person delay for bus and in some cases even auto users

Cost/benefit trade-offs for reducing the energy consumption of commercial air transportation (RECAT)

A study has been performed to evaluate the opportunities for reducing the energy requirements of the U.S. domestic air passenger transport system through improved operational techniques, modified in-service aircraft, derivatives of current production models, or new aircraft using either current or advanced technology. Each of the fuel-conserving alternatives has been investigated individually to test its potential for fuel conservation relative to a hypothetical baseline case in which current, in-production aircraft types are assumed to operate, without modification and with current operational techniques, into the future out to the year 2000