Comparing travel mode and trip chain choices between holidays and weekdays

Choices of travel mode and trip chain as well as their interplays have long drawn the interests of researchers. However, few studies have examined the differences in the travel behaviors between holidays and weekdays. This paper compares the choice of travel mode and trip chain between holidays and weekdays tours using travel survey data from Beijing, China. Nested Logit (NL) models with alternative nesting structures are estimated to analyze the decision process of travelers. Results show that there are at least three differences between commuting-based tours on weekdays and non-commuting tours on holidays. First, the decision structures in weekday and holiday tours are opposite. In weekday tours people prefer to decide on trip chain pattern prior to choosing travel mode, whereas in holiday tours travel mode is chosen first. Second, holiday tours show stronger dependency on cars than weekday tours. Third, travelers on holidays are more sensitive to changes in tour time than to the changes in tour cost, while commuters on weekdays are more sensitive to tour cost. Findings are helpful for improving travel activity modeling and designing differential transportation system management strategies for weekdays and holidays.     

Commuters’ behavior towards upgraded bus services in Greater Beirut: Implications for greenhouse gas emissions,social welfare and transport policy

Climate change is one of the most critical environmental challenges faced in the world today. The transportation sector alone contributes to 22% of carbon emissions, of which 80% are contributed by road transportation. In this paper we investigate the potential private car greenhouse gas (GHG) emissions reduction and social welfare gains resulting from upgrading the bus service in the Greater Beirut Area. To this end, a stated preference (SP) survey on mode switching from private car to bus was conducted in this area and analyzed by means of a mixed logit model. We then used the model outputs to simulate aggregate switching behavior in the study area and the attendant welfare and environmental gains and private car GHG emissions reductions under various alternative scenarios of bus service upgrade. We recommend a bundle of realistic bus service improvements in the short term that will result in a reasonable shift to buses and measurable reduction in private car emissions. We argue that such improvements will need to be comprehensive in scope and include both improvements in bus level of service attributes (access/egress time, headway, in-vehicle travel time, and number of transfers) and the provision of amenities, including air-conditioning and Wi-Fi. Moreover, such a service needs to be cheaply priced to achieve reasonably high levels of switching behavior. With a comprehensively overhauled bus service, one would expect that bus ridership would increase for commuting purposes at first, and once the habit for it is formed, for travel purposes other than commuting, hence dramatically broadening the scope of private car GHG emissions reduction. This said, this study demonstrates the limits of focused sectorial policies in targeting and reducing private car GHG emissions, and highlights the need for combining behavioral interventions with other measures, most notably technological innovations, in order for the contribution of this sector to GHG emissions mitigation to be sizable.     

Why do some motorbike riders wear a helmet and others don’t? Evidence from Delhi,India

Road traffic accident fatalities lead to important private and social costs in the metropolitan areas of most low and middle income countries. An important share of these fatalities is due to injuries to the head and the neck. Helmets can provide efficient protection, but many drivers do not use them. We focus on helmet use behavior among motorbike users in Delhi. We use a detailed data set collected for the purpose of the study. To guide our empirical analysis, we rely on a model in which drivers decide on self-protection and self-insurance. The empirical findings suggest that risk-averse drivers are more likely to wear a helmet and that this has no systematic effect on speed. Helmet use also increases with education. Drivers who show a higher awareness of road risks seem to be both more likely to wear a helmet and to speed less. Controlling for risk awareness, we observe that drivers tend to compensate between speed and helmet use. The results can provide a basis for awareness-raising policies. They also show that improvements to the road infrastructure risk leading to risk-compensating behavior.     

Mining missing train logs from Smart Card data

This paper shows how to recover the arrival times of trains from the gate times of metro passengers from Smart Card data. Such technique is essential when a log, the set of records indicating the actual arrival and departure time of each bus or train at each station and also a critical component in reliability analysis of a transportation system, is missing partially or entirely. The procedure reconstructs each train as a sequence of the earliest exit times, called S-epochs, among its alighting passengers at each stations. The procedure first constructs a set of passengers, also known as reference passengers, whose routing choices are easily identifiable. The procedure then computes, from the exit times of the reference passengers, a set of tentative S-epochs based on a detection measure whose validity relies on an extreme-value characteristic of the platform-to-gate movement of alighting passengers. The tentative S-epochs are then finalized to be a true one, or rejected, based on their consistencies with bounds and/or interpolation from prescribed S-epochs of adjacent trains and stations. Tested on 12 daily sets of trains, with varying degrees of missing logs, from three entire metro lines, the method restored the arrival times of 95% of trains within the error of 24 s even when 100% of logs was missing. The mining procedure can also be applied to trains operating under special strategies such as short-turning and skip-stop. The recovered log seems precise enough for the current reliability analysis performed by the city of Seoul.     

Influence of priority taking and abstaining at single-lane roundabouts using cellular automata

Existing roundabout simulation models fail to consider all types of driver behavior which compromises their accuracy and ability to accurately evaluate roundabout performance. Further, these non-compliant driver behaviors, including priority taking and priority abstaining, are inconsistent with existing traffic flow theories. In this paper, a new cellular automata model, C.A.Rsim, is developed and calibrated with field data from five single-lane roundabouts in four northeastern states. Model results indicate that approximately 20% of the individuals in the driver population are inclined to priority taking and approximately 20% are inclined to priority abstaining behavior, though the observed levels of these types of behavior are naturally lower and vary with traffic volume. The model results also corroborate other research indicating that current models can overestimate capacity at higher circulating volumes, possibly a result of the jamming effect produced by priority taking behavior. The reduction in priority abstaining behavior, which is observed at older roundabouts, significantly reduces delay and queue length in certain traffic volumes. C.A.Rsim is also more parsimonious than many existing microsimulation models. These results provide insight on how variations in conflicting flow (i.e., traffic volume and turning movement balance) impact the amount of observed non-compliant behavior.     

Optimal locations and travel time display for variable message signs

This paper first develops a network equilibrium model with the travel time information displayed via variable message signs (VMS). Specifically, the equilibrium considers the impact of the displayed travel time information on travelers’ route choices under the recurrent congestion, with the endogenous utilization rates of displayed information by travelers. The existence of the equilibrium is proved and an iterative solution procedure is provided. Then, we conduct the sensitivity analyses of the network equilibrium and further propose a paradox, i.e., providing travel time information via VMS to travelers may degrade the network performance under some poor designs. Therefore, we investigate the problem of designing the VMS locations and travel time display within a given budget, and formulate it as a mixed integer nonlinear program, solved by an active-set algorithm. Lastly, numerical examples are presented to offer insights on the equilibrium results and optimal designs of VMS.     

Forecasting travel behavior using Markov Chains-based approaches

Recent advances in agent-based micro-simulation modeling have further highlighted the importance of a thorough full synthetic population procedure for guaranteeing the correct characterization of real-world populations and underlying travel demands. In this regard, we propose an integrated approach including Markov Chain Monte Carlo (MCMC) simulation and profiling-based methods to capture the behavioral complexity and the great heterogeneity of agents of the true population through representative micro-samples. The population synthesis method is capable of building the joint distribution of a given population with its corresponding marginal distributions using either full or partial conditional probabilities or both of them simultaneously. In particular, the estimation of socio-demographic or transport-related variables and the characterization of daily activity-travel patterns are included within the framework. The fully probabilistic structure based on Markov Chains characterizing this framework makes it innovative compared to standard activity-based models. Moreover, data stemming from the 2010 Belgian Household Daily Travel Survey (BELDAM) are used to calibrate the modeling framework. We illustrate that this framework effectively captures the behavioral heterogeneity of travelers. Furthermore, we demonstrate that the proposed framework is adequately adapted to meeting the demand for large-scale micro-simulation scenarios of transportation and urban systems.     

Multi-step prediction of experienced travel times using agent-based modeling

This paper develops an agent-based modeling approach to predict multi-step ahead experienced travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in a decision-making system. Each expert predicts the travel time for each time interval according to experiences from a historical dataset. A set of agent interactions is developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents associated with negligible weights with new agents. Consequently, the aggregation of each agent’s recommendation (predicted travel time with associated weight) provides a macroscopic level of output, namely the predicted travel time distribution. Probe vehicle data from a 95-mile freeway stretch along I-64 and I-264 are used to test different predictors. The results show that the agent-based modeling approach produces the least prediction error compared to other state-of-the-practice and state-of-the-art methods (instantaneous travel time, historical average and k-nearest neighbor), and maintains less than a 9% prediction error for trip departures up to 60 min into the future for a two-hour trip. Moreover, the confidence boundaries of the predicted travel times demonstrate that the proposed approach also provides high accuracy in predicting travel time confidence intervals. Finally, the proposed approach does not require offline training thus making it easily transferable to other locations and the fast algorithm computation allows the proposed approach to be implemented in real-time applications in Traffic Management Centers.     

波浪中破损船舶的运动响应研究

波浪中破损船舶的运动会同时受到波浪激励和进出水的影响,而船体运动也会影响进出水过程,二者的相互影响机理十分复杂.本文重点研究波浪中破舱进出水对船舶运动响应的影响,文中首先基于势流理论建立了考虑破舱进出水的4DOF(横荡-垂荡-横摇-纵摇)相互耦合时域预报方法,在计算中假设舱内的液面水平,利用修正的伯努利方程模拟破舱进/出水,利用Ikeda's经验公式修正阻尼系数.然后以一艘ITTC破损稳性标模为例,研究了波浪中考虑破舱进出水的数学模型以及破舱进出水对运动响应的影响,并研究了不同自由度、破舱口位置对运动响应的影响.研究表明,本文基于势流理论建立的时域预报方法可以定量的预报破损船舶的运动响应.     

航海学子抑郁及其相关影响因素的研究

采用抑郁自评量表等对186名航海学子进行调查.不同地区、不同年龄、不同年级的航海学子抑郁水平没有显著的差异;抑郁与人格和应对方式有显著的相关关系;进一步回归分析结果表明,有8个因素对抑郁的预测作用十分明显,具体为人格因素的神经质、应对方式的喝酒、积极思维、计划、回避、情感支持、工具性支持、主动应对.