Distribution Optimization Model for Passenger Departure via Multimodal Transit

International airports in China have become a complex hub between airport and multimodal transit stations. Dissimilar passenger departure demands in different transit mode cause wide gaps among departure times from airport to these modes. In this context, hub managers need to balance the distribution of air passengers to transit modes in order to reduce departure delays and alleviate the congestion in transit stations, even though they cannot change the operating plan of airport or transit stations. However, few research efforts have addressed this distribution. Therefore, we developed a distribution optimization model for passenger departure that minimizes the average departure time and is solved by Genetic Algorithm. To describe differences in passenger choices, without taking into consideration the metropolitan transportation network outside the airport, we introduced the concept of rigid and elastic departures. To reflect the tendency of elastic passengers to choose different transit modes, we assume that the passengers change to other modes in different proportions. A case revealed that the presence of rigid passengers allows managers to partly balance the distribution of passengers and improve the average departure time. When the volume of passengers approaches the peak volume, the optimized distribution significantly improves the departure time

Heteroaggregation of nanoparticles with biocolloids and geocolloids

The application of nanoparticles has raised concern over the safety of these materials to human health and the ecosystem. After release into an aquatic environment, nanoparticles are likely to experience heteroaggregation with biocolloids, geocolloids, natural organic matter (NOM) and other types of nanoparticles. Heteroaggregation is of vital importance for determining the fate and transport of nanoparticles in aqueous phase and sediments. In this article, we review the typical cases of heteroaggregation between nanoparticles and biocolloids and/or geocolloids, mechanisms, modeling, and important indicators used to determine heteroaggregation in aqueous phase. The major mechanisms of heteroaggregation include electric force, bridging, hydrogen bonding, and chemical bonding. The modeling of heteroaggregation typically considers DLVO, X-DLVO, and fractal dimension. The major indicators for studying heteroaggregation of nanoparticles include surface charge measurements, size measurements, observation of morphology of particles and aggregates, and heteroaggregation rate determination. In the end, we summarize the research challenges and perspective for the heteroaggregation of nanoparticles, such as the determination of αhetero values and heteroaggregation rates; more accurate analytical methods instead of DLS for heteroaggregation measurements; sensitive analytical techniques to measure low concentrations of nanoparticles in heteroaggregation systems; appropriate characterization of NOM at the molecular level to understand the structures and fractionation of NOM; effects of different types, concentrations, and fractions of NOM on the heteroaggregation of nanoparticles; the quantitative adsorption and desorption of NOM onto the surface of nanoparticles and heteroaggregates; and a better understanding of the fundamental mechanisms and modeling of heteroaggregation in natural water which is a complex system containing NOM, nanoparticles, biocolloids and geocolloids

Dynamic Holding Strategy to Prevent Buses from Bunching

This study proposed a robust dynamic control strategy to regulate bus headways and prevent buses from bunching by holding them at bus stops. The proposed strategy monitors bus locations in real time and estimates the time gaps between consecutive buses at a desired frequency. The holding times of all buses at their respective immediately downstream stops are determined simultaneously on the basis of the estimated time gaps. A procedure that consists of a discrete quadratic dynamic control program and a quadratic static optimization program was developed to produce a unique dynamic control law of holding times. Numerical investigations on an operational bus route revealed that the proposed strategy produced greater system reliability than did some existing control strategies and that the bus system under the control of the proposed strategy recovered promptly from large system disruptions

Bus Drivers\u27 Responses to Real-Time Schedule Adherence and the Effects on Transit Reliability

Bus drivers have responded positively toward real-time bus information in various surveys. However, empirical studies on their actual responses are limited. On the basis of actual automatic vehicle location data, this study quantified bus drivers\u27 responses to real-time schedule adherence and their effects on transit reliability. Bus trips that were ahead of and behind schedule were analyzed separately at timepoint stops, regular stops, and along the roadways between stops. Results revealed that bus drivers would use real-time information to keep on schedule. Early buses were found to be more likely to make adjustments in response to information than were late buses along the roadways. Moreover, bus drivers\u27 responses to real-time information was found to improve transit reliability: 50% of the improvement was the result of drivers\u27 responses to schedule adherence at timepoint stops and 50% was the result mainly of drivers\u27 responses to schedule adherence along the roadways. The likelihood that drivers would make adjustments at regular stops to adhere to schedule was low

Coordinated optimization of tram trajectories with arterial signal timing resynchronization

Modern trams run on exclusive rail lanes along urban streets, but they usually share the right of way with general traffic at intersections and often get interrupted by traffic signals. To improve tram operation reliability, this paper develops a methodology to optimize a multi-period tram timetable by simultaneously adjusting bidirectional scheduled tram trajectories and traffic signal timings. The objective balances the operational priorities of minimizing tram running time and maximizing timetable adherence. The scheduled trajectories depict tram movements along the roads and dwell processes at stations. Arterial signal timings are resynchronized to favor tram movements. The proposed methodology was evaluated in a simulation of a real-world tram line. Compared with a traditional approach, the proposed methodology reduced tram running time, number of stops at intersections and schedule delay by 11.1%, 82.4% and 37.5%, respectively. The impact on general traffic could be assumed neutral since cycle lengths, green splits and vehicular bandwidths were kept unchanged

Estimating Transit Route OD Flow Matrices from APC Data on Multiple Bus Trips Using the IPF Method with an Iteratively Improved Base: Method and Empirical Evaluation

An iterative method is proposed to estimate bus route origin-destination (OD) passenger flow matrices from boarding and alighting data for time-of-day periods in the absence of good a priori estimates of the flows. The algorithm is based on the widely used iterative proportional fitting (IPF) method and takes advantage of the large quantities of boarding and alighting data that are routinely collected by transit agencies using automatic passenger count (APC) technologies. An arbitrarily chosen OD matrix can be used as the base matrix required to initialize the algorithm, and the IPF method is applied with bus trip-level boarding and alighting data and the base matrix to produce an estimate of the OD flow matrix for each bus trip. The trip-level OD flow matrices are then aggregated to produce an estimate of the period-level OD flow matrix, which in turn is used as the base matrix for the following iteration. The process is repeated until convergence. Empirical results are conducted on operational bus routes using APC data collected during multiple season-years, where directly observed OD passenger flows are available to represent the ground truth. In all cases in which APC data are available for even a reasonably small number of bus trips, the iteratively improved base method produces better estimates than the application of the traditional IPF method when using a null base matrix, which is commonly adopted in the absence of a priori information without updating. Moreover, the algorithm converges in minimal computational time to the same estimates regardless of the initializing matrices used. Read More: http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29TE.1943-5436.000064

Transit passenger origin–destination flow estimation: Efficiently combining onboard survey and large automatic passenger count datasets

As transit agencies increasingly adopt the use of Automatic Passenger Count (APC) technologies, a large amount of boarding and alighting data are being amassed on an ongoing basis. These datasets offer opportunities to infer good estimates of passenger origin–destination (OD) flows. In this study, a method is proposed to estimate transit route passenger OD flow matrices for time-of-day periods based on OD flow information derived from labor-intensive onboard surveys and the large quantities of APC data that are becoming available. The computational feasibility of the proposed method is established and its accuracy is empirically evaluated using differences between the estimated OD flows and ground-truth observations on an operational bus route. To interpret the empirical differences from the ground-truth estimates, differences are also computed when using the state-of-the-practice Iterative Proportional Fitting (IPF) method to estimate the OD flows. The empirical results show that when using sufficient quantities of boarding and alighting data that can be readily obtained from APC-equipped buses, the estimates determined by the proposed method are better than those determined by the IPF method when no or a small sample sized onboard OD flow survey dataset is available and of similar quality to those determined by the IPF method when a large sample sized onboard OD flow survey dataset is available. Therefore, the proposed method offers the opportunity to forgo conducting costly onboard surveys for the purpose of OD flow estimation

Tram-Oriented Traffic Signal Timing Resynchronization

Modernized trams usually run on exclusive rail lanes along urban streets, but they share the right of way with general vehicles at intersections and often get interrupted by traffic signals. We developed a mixed integer model to resynchronize traffic signal timings to favor tram movements. The objective is to balance the operational needs between minimizing bidirectional tram travel times and reducing the likelihood of activating the green extensions. The model depicts both tram and vehicle progressions in one signal timing plan, making it possible to control the impact of signal timing resynchronization through traffic. Trams following the tram bands produced by the proposed model are prevented from being stopped by red phases at signalized intersections. The applicability and effectiveness of the proposed model were demonstrated in a real-world case study. Compared with the state-of-the-art practice approach, the developed model reduced tram travel time by 10% with lower negative impacts on traffic on side streets. The reduction in tram travel time was obtained without sacrificing the mobility of through traffic

Estimating Bus Loads and OD Flows Using Location-Stamped Farebox and Wi-Fi Signal Data

Electronic fareboxes integrated with Automatic Vehicle Location (AVL) systems can provide location-stamped records to infer passenger boarding at individual stops. However, bus loads and Origin-Destination (OD) flows, which are useful for route planning, design, and real-time controls, cannot be derived directly from farebox data. Recently, Wi-Fi sensors have been used to collect passenger OD flow information. But the data are insufficient to capture the variation of passenger demand across bus trips. In this study, we propose a hierarchical Bayesian model to estimate trip-level OD flow matrices and a period-level OD flow matrix using sampled OD flow data collected by Wi-Fi sensors and boarding data provided by fareboxes. Bus loads on each bus trip are derived directly from the estimated trip-level OD flow matrices. The proposed method is evaluated empirically on an operational bus route and the results demonstrate that it provides good and detailed transit route-level passenger demand information by combining farebox and Wi-Fi signal data