Effect of Traffic Reports on Commuters' Route and Departure Time Changes

Poisson analysis of commuter flexibility in changing routes and departure times

This paper documents the efforts to operationalize the conceptual framework of MIcrosimulation Learning-based Approach to TRansit Assignment (MILATRAS) and its component models of departure time and path choices. It presents a large-scale real-world application, namely the multi-modal transit network of Toronto which is operated by the Toronto Transit Commission (TTC). This large-scale network is represented by over 500 branches with more than 10,000 stops. About 332,000 passenger-agents are modelled to represent the demand for the TTC in the AM peak period. A learning-based departure time and path choice model was adopted using the concept of mental models for the modelling of the transit assignment problem. The choice model parameters were calibrated such that the entropy of the simulated route loads was optimized with reference to the observed route loads, and validated with individual choices. A Parallel Genetic Algorithm engine was used for the parameter calibration process. The modelled route loads, based on the calibrated parameters, greatly approximate the distribution underlying the observed loads. 75% of the exact sequence of transfer point choices were correctly predicted by the off-stop/on-stop choice mechanism. The model predictability of the exact sequence of route transfers was about 60%. In this application, transit passengers were assumed to plan their transit trip based on their experience with the transportation network; with no prior (or perfect) knowledge of service performance.

<div class="section abstract"><div class="htmlview paragraph">As modern society develops rapidly, people’s requests for traffic convenience and traffic safety become greater and greater, and it is essential to eliminate traffic congestion and traffic accident to sustainable development of urban areas. Therefore, this paper brings forward novel solution based on hybrid sensor networks to observe the status of traffic in road networks in order to alleviate traffic jam and prevent traffic accident. With the collection of precise traffic flow information at the time, it realizes traffic flow control at crossroads, gives warning in advance with the congestion or accident. We carried out a bunch of simulation experiments in succession, the main discoveries are as follows. a. The energy consumption is great reduced under the sensor deployment rate between 1:50–1:60 (sensor : vehicles). b.The sampling rates can keep a very high level of precise and efficiency under the appropriate range between 1:50–1:60 (sensor : vehicles).The critical segments of roadways are fitted with the radar sensors to accomplish not only reliable surveillance of traffic congestions but also timeearlies warning of traffic accidents as opposed to relying on the single-sensor network. As reflected in Fig. 17, the heterogeneous sensor network is more robust against sensor errors because of the complementarity effects without relying on individual sensors. The experimental results highlight the potential for hybrid sensing architecture for intelligent transportation systems(ITS) and provides a well-technical basis to alleviate urban traffic jam and improve transportation efficiency.</div></div>

<div class="section abstract"><div class="htmlview paragraph">Airline passenger satisfaction is important for airline operation service quality management. When airline companies carry out advertisement campaigns or plan a marketing strategy, the resources and budgets are not unlimited. Thus, an airline can only focus on improving a few factors that drive passenger satisfaction. To understand the key satisfies for the young and the old adults, respectively, we leverage five airline passenger satisfaction methods to identify the key factors that explain the airline service satisfaction of different passengers. In particular, we investigate and compare the ridge and the Lasso regularization in terms of the resulting model’s sparsity and computational efficiency. The top three important factors that influence the old’s satisfaction are departure and arrival time convenience, legroom service, and baggage handling. Our findings indicate that the young people place a higher value on entertainment, while the old adults place a higher value on usefulness and comfort. The Lasso is the most accurate model with the overall error of 9.65% to predict the young passenger’s satisfaction, while the Best Subset with BIC with the overall error of 10% is the best mode for the old adults. It’s suggested that airline companies could use the Lasso model for predicting the airline satisfaction of the young people, and use the best subset with BIC for predicting the airline satisfaction of the old adults. The study findings would help the airlines improving their state-of-the-art operations to have outstanding service.</div></div>

<div class="htmlview paragraph">The results of three studies, each designed to obtain functional requirements for the design of a graphics-based traveler information system, are reported. In the first study, the results of a survey are reviewed in which four commuter groups are identified with regard to the impact of traffic information on mode choice, departure time, and alternate route decisions. In the second study, the usability of five prototype traffic information screens are reported. In the last study, the results of a phone interview designed to provide an in-depth understanding of commuter traffic information needs is discussed.</div>

At large airports, aircraft takeoff and land simultaneously on multiple runways. As a result, taxiing is a complex and, at times, lengthy process. Planning for aircraft taxiing is an essential design element for the expansion of runways, taxiways, and terminals. Unnecessary taxiing should be reduced. In this study, we develop a taxiing model and calculate the taxiing time. We observed aircraft traffic patterns and listened to the communication of air traffic controllers (ATCs) in order to develop a taxiing model. The developed model was applied to Narita International Airport. We begin by estimating the time required for taxiing at Narita International for 2014. Next, we propose two strategies to reduce the taxiing time. The first strategy involves changes in departure times with controlled intervals between them. In this strategy, the average taxiing time is reduced by 16.5%. The other strategy requires adjustments in the time from the aircraft departure from the terminal to the start of taxiing. In this strategy, the maximum taxiing time is reduced by 11.5%, and the intervals between the terminal departures and taxiing were more uniform.

How the migration behavior of long-lived birds develops throughout their life is still a research question in ornithology that is largely unanswered. Here, we analyzed GPS data of 70 juvenile Black Storks during the first 3 years of their migration to determine the influence of age and experience on migration patterns. We focused on the choice of the migration flyway, as well as the movements in the wintering area, and the changes in departure times with increasing age. Migratory routes of German Black Storks were divided into the western (82% of all tracked individuals) and eastern flyways (16%). The central migratory flyway through Italy or Greece was taken only by two individuals and never repeatedly. In addition, we found that the wintering area decreased by almost 60% during their first 3 years of life. In addition, spring departure dates from the wintering areas also became earlier with increasing age. However, we did not find changes in departures from the breeding area in autumn. Thus, our study shows that age and migration experiences have a considerable influence on essential migratory decisions in Black Storks. Further, it also demonstrates that older storks rely strongly on previous experiences to perform their migrations efficiently.

Kualanamu International Airport Kab. Deli Serdang as one of the entry gates for domestic or international tourists to North Sumatra, which was previously at Polonia Medan Airport, which is managed by Angkasa Pura II, greatly influences the comfort of service users through the information provided to passengers so that passengers feel comfortable while waiting for departure. aircraft. By looking at the relatively large interest of users of air transportation services, they require services at Angkasa Pura II Airport which involve effective and efficient public relations to bring potential service users back. In addition, this public relations approach greatly stimulates the interest of service users by providing information services, flight schedules, changes in departure times, and others until service users are satisfied and bring in other service users at Kualanamu International Airport Kab. Deli Serdang. Keywords: Public Relations, a

In Mobile Adhoc networks (MANET) the mobility of nodes results in regular and random changes of network topology, which results in regular route changes, network partitions and possibly packet losses, making routing a challenging task in MANET network. The mostly used routing protocols in such networks are proactive and reactive routing protocols, but a reactive routing strategy is the most popular technique for wireless adhoc routing that provides a scalable solution to relatively large network topologies. However Hybrid GRP (Geographical Routing Protocol) are introduced comprises of both reactive and proactive routing properties to give high QoS parameter performance. This paper evaluates the performance of AODV- and DSR reactive routing protocols with hybrid GRP protocol in MANET network using QoS parameters like End-to-End Delay, Network Load, Throughput, Number of Hops/Route, PDR and NRL.

Bus timetables with even headways often require passengers to adjust to given timetables instead of adjusting the timetables to fluctuating passenger demand. This expectation is a main source of unreliable service that extends passenger waiting times at bus stops and creates overcrowding. Alternative transit timetables are created to improve the correspondence of vehicle departure times with passenger demand while minimizing resources (number of buses required). An automated method is presented. With the growing problems of transit reliability, and the advance in the technology of passenger information systems, even and clock headways become less important. Therefore, more efficient schedules from both the passenger and operator perspectives can be realized. The method presented uses an informative graphic technique. This approach enables the scheduler to examine changes in bus departure times and to obtain immediate feedback on the expected average passenger loads.

The exclusive bus lane (XBL) is one of the most popular bus transit systems in the U.S. The Lincoln Tunnel utilizes an XBL through the tunnel in the AM peak period. This paper proposes a novel data-driven cooperative adaptive cruise control (CACC) algorithm that aims to minimize a cost function for connected and autonomous buses along the XBL. Different from existing model-based CACC algorithms, the proposed approach employs the idea of reinforcement learning, which does not rely on accurate knowledge of bus dynamics. Considering a time-varying topology, where each autonomous vehicle can only receive information from preceding vehicles that are within its communication range, a distributed controller is learned real-time by online headway, velocity, and acceleration data collected from the system trajectories. The convergence of the proposed algorithm and the stability of the closed-loop system are rigorously analyzed. The effectiveness of the proposed approach is demonstrated using a well-calibrated Paramics microscopic traffic simulation model of the XBL corridor. The simulation results show that the travel time in the autonomous version of the XBL are close to the present day travel time even when the bus volume is increased by 30%.

Streets have long been designed to maximize motor vehicle throughput, ignoring other street users. Many cities are now reversing this trend and implementing policies to design more equitable streets. However, few existing tools and metrics enable widescale assessment, evaluation, and longitudinal tracking of these street space rebalancing efforts, i.e., assessing how equitable the current street design is, how it can be improved, and how much progress has been made. This paper develops a needs-gap methodology for assessing the discrepancy between transportation supply and demand in urban streets using existing datasets and automated methods. The share of street space allocated to different street users is measured in 11 boroughs of Montréal, Canada. Travel survey data is used to estimate the observed and potential travel demand in each borough in the AM peak period. A needs-gap analysis is then carried out. It is found that bus riders and cyclists face the greatest needs-gap across the study area, especially in central boroughs. The needs-gap also increases if only trips produced or attracted by a borough are considered. This shows the potential of applying an equity-based framework to the automated assessment of street space allocation in cities using large datasets.

As they involve many interacting agents behaving in numerous ways that are extremely difficult to predict, urban transportation systems are complex in nature. The development of intermodal passenger transportation solutions to address the mobility issues constitutes a major thrust area of urban transport policies. But, to offer citizens comprehensive seamless mobility, intermodal transportation system management (ITSM) requires the integration of two major components. The traffic regulation support system, to help the operator responsible for the regulation tasks: coordination of timetables, synchronising arrival and departure times between the different transportation modes, and the traveller information system, giving customers access to information and using a comprehensive set of information tools. In this paper, a generic model of a transport management system, integrating these two components is proposed. This generic model is then used to elaborate a traffic regulation system in the case of a bimodal transportation system (tram-bus). The traffic regulation support system, based on the decision model of an operator, and the traveler information system are described.

One of the key functions of an effective Advanced Traveler and Management Information System is the ability to model short-term predictions of traffic conditions on major freeways and arterials with reasonable accuracy. The impetus of forecasting traffic information rather than relying on real-time information is to allow the traveling public to be proactive in their travel decisions at both pretrip planning stage and en route. Traffic information is usually reflected by the traditional performance measures such as travel times and delays. Such measures are well perceived by the traveling public and, hence, are adopted in most short-term traffic prediction studies. When disseminated to the traveling public, forecasted travel time information will help the travelers make better trip decisions in terms of departure time, route selection, and mode selection, when transit becomes competitive. Several traffic prediction models have been developed in the past using a wide spectrum of modeling techniques. Most studies showed that prediction accuracy is often compromised by the underlying mechanism of prediction methods more than other influencing factors. The main objective of this study is to investigate the factors that have a significant impact on the forecasting accuracy of travel times using a nonlinear time series traffic prediction model. The ultimate goal is to identify the operational settings and the anticipated forecasting accuracy of the model before it can be fully implemented. The study was conducted using extensive amount of real-time data collected from the 62.5 km corridor of Interstate-4 in Orlando, Florida. Various scenarios were generated from a combination of model parameter settings and different traffic conditions in order to test the performance of the model. Relative travel time prediction errors were used as a measure of performance and were based on speed predictions at freeway detector stations. Statistical analysis was conducted to identify the parameters with a statistically significant effect on the model's performance.

The application of information and communication technologies in transport