Short-term work team scheduling models for effective road repair and management

Abstract

In this study, we focus on the development of work team routing/scheduling models incorporating stochastic travel and repair times. Robust and expected optimization concepts, combined with a time–space network technique, are used to develop the models. We perform numerical tests based on operational data for Taoyuan County in Taiwan. The test results show the good performance of the models.     

Application of Lagrangian relaxation approach to α-reliable path finding in stochastic networks with correlated link travel times

This study investigates the important problem of determining a reliable path in a stochastic network with correlated link travel times. First, the distribution of path travel time is quantified by using trip records from GPS probe vehicles. Second, the spatial correlation of link travel time is explicitly considered by using a correlation coefficient matrix, which is incorporated into the α-reliable path problem by Cholesky decomposition. Third, the Lagrangian relaxation based framework is used to handle the α-reliable path problem, by which the intractable problem with a non-linear and non-additive structure can be decomposed into several easy-to-solve problems. Finally, the path-finding performance of this approach is tested on a real-world network. The results show that 15 iterations of calculation can yield a small relative gap between upper and lower bounds of the optimal solution and the average running time is about 5 s for most OD settings. The applicability of α-reliable path finding is validated by a case study.     

An adaptive information fusion model to predict the short-term link travel time distribution in dynamic traffic networks

As intelligent transportation systems (ITS) approach the realm of widespread deployment, there is an increasing need to robustly capture the variability of link travel time in real-time to generate reliable predictions of real-time traffic conditions. This study proposes an adaptive information fusion model to predict the short-term link travel time distribution by iteratively combining past information on link travel time on the current day with the real-time link travel time information available at discrete time points. The past link travel time information is represented as a discrete distribution. The real-time link travel time is represented as a range, and is characterized using information quality in terms of information accuracy and time delay. A nonlinear programming formulation is used to specify the adaptive information fusion model to update the short-term link travel time distribution by focusing on information quality. The model adapts good information by weighing it higher while shielding the effects of bad information by reducing its weight. Numerical experiments suggest that the proposed model adequately represents the short-term link travel time distribution in terms of accuracy and robustness, while ensuring consistency with ambient traffic flow conditions. Further, they illustrate that the mean of a representative short-term travel time distribution is not necessarily a good tracking indicator of the actual (ground truth) time-dependent travel time on that link. Parametric sensitivity analysis illustrates that information accuracy significantly influences the model, and dominates the effects of time delay and the consistency constraint parameter. The proposed information fusion model bridges key methodological gaps in the ITS deployment context related to information fusion and the need for short-term travel time distributions.     

Modeling Travel Time Under ATIS Using Mixed Linear Models

The objective of this paper is to model travel time when drivers are equipped with pre-trip and/or en-route real-time traffic information/advice. A travel simulator with a realistic network and real historical congestion levels was used as a data collection tool. The network included 40 links and 25 nodes. This paper presents models of the origin-to-destination travel time and en-route short-term route (link) travel time under five different types and levels of advanced traveler information systems (ATIS). Mixed linear models with the repeated observation's technique were used in both models. Different covariance structures (including the independent case) were developed and compared. The effect of correlation was found significant in both models. The trip travel time analysis showed that as the level of information increases (adding en-route to the pre-trip and advice to the advice-free information), the average travel time decreases. The model estimates show that providing pre-trip and en-route traffic information with advice could result in significant savings in the overall travel time. The en-route short-term (link) travel time analysis showed that the en-route short-term (link) information has a good chance of being used and followed. The short-term qualitative information is more likely to be used than quantitative information. Learning and being familiar with the system that provides the information decreases en-route short-term delay.     

Floating car based travel times for city logistics

City logistics routing requires time-dependent travel times for each network link. We rely on the concept of Floating Car Data (FCD) to develop and provide such travel times. Different levels of aggregation in the determination of time-dependent travel times from a database of historical FCD are presented and evaluated with regard to routing quality. Furthermore, a Data Mining approach is introduced, allowing for a substantial reduction of the volume of input data required for city logistics routing. The different approaches are investigated and evaluated by a huge amount of FCD collected for the urban area of Stuttgart, Germany. The results show that the Data Mining approach enables efficient provision of time-dependent travel times without a significant loss of routing quality for city logistics applications.     

A stochastic optimization model for transit network timetable design to mitigate the randomness of traveling time by adding slack time

Transit network timetabling aims at determining the departure time of each trip of all lines in order to facilitate passengers transferring either to or from a bus. In this paper, we consider a bus timetabling problem with stochastic travel times (BTP-STT). Slack time is added into timetable to mitigate the randomness in bus travel times. We then develop a stochastic integer programming model for the BTP-STT to minimize the total waiting time cost for three types of passengers (i.e., transferring passengers, boarding passengers and through passengers). The mathematical properties of the model are characterized. Due to its computational complexity, a genetic algorithm with local search (GALS) is designed to solve our proposed model (OPM). The numerical results based on a small bus network show that the timetable obtained from OPM reduces the total waiting time cost by an average of 9.5%, when it is tested in different scenarios. OPM is relatively effective if the ratio of the number of through passengers to the number of transferring passengers is not larger than a threshold (e.g., 10 in our case). In addition, we test different scale instances randomly generated in a practical setting to further verify the effectiveness of OPM and GALS. We also find that adding slack time into timetable greatly benefits transferring passengers by reducing the rate of transferring failure.     

Factors influencing travel mode choice among families with young children (aged 0–4): a review of the literature

Life events, such as the birth of a child, disrupt habitual travel behaviour and provide a valuable opportunity to influence the adoption of sustainable transport practices. However, in order for sustainable travel practices to be adopted, an understanding is required of the factors that influence travel mode choice among families with young children. Research in this field is particularly timely given many in the millennial generation, a comparably large cohort, are approaching this life stage. This comprehensive literature review develops a framework of factors influencing travel mode choice among families with young children. The findings reveal a multitude of factors influence decisions about mode choice, and, in particular, encourage travel by car, when travelling with young children. The paper concludes with an agenda for future research about travel among families with young children, a largely overlooked group of transport users.     

Things planners believe in,and things they deny

The paper examines the economic theory of consumer behavior from the Modern Psychoanalytic point of view with a metaphor of (transport) planning as a backdrop. It is claimed that the utility maximization principle of economic theory, no matter how broadly interpreted, does not envelop the motivation of human behavior and that the economic explanation of the aims of human behavior, which disregards the "dark side" of man, will result in narrowly conceived plans and policies.The application of economic theory, and other social sciences, to public policy has led to disjointedness of data collection, method of analysis, and planning and implementation. This, combined with a tautological behavioral theory, has made the current planning and public policy procedures self-verifying, and issues an invitation for a new theoretical approach which is comprehensive, does not ignore complexity and the large "error term", and appreciates the unity of data collection, method of investigation, and process of implementation.It is argued that unsatisfactory performance of the transport system, large errors inherent in data and models, the inability of extant transportation planning and policy to address key issues successfully, the environmental problem, and the value of the individual all call for new kinds of skills from the planners and policy makers.Freud's dualistic formulation of instinctual drives -- the life and death instincts -- is used as the new framework of explanation. This theory is seen to offer a convincing, more realistic and deeper understanding of the forces motivating human behavior, and one which is more in accordance with observable facts. If applied, the theory will lead to significantly different planning processes and to a profoundly different approach to public policy.     

Over-reporting vs. overreacting: Commuters’ perceptions of travel times

In a large-scale, real-life peak avoidance experiment, we asked participants to provide estimates of their average in-vehicle travel time during their morning commute. After comparing the reported travel times with the actual corresponding travel times, we found that the average travel times were overstated by a factor of 1.5. We showed that driver- and link-specific characteristics partially explained these exaggerations. Using the stated and revealed preference data, we investigated whether the driver-specific reporting errors were consistent with the drivers’ scheduling behaviors in reality and in hypothetical choice experiments. In both cases, we found no robust evidence that drivers behave as if they misperceive travel times to a similar extent as those they misreported, thereby implying that the reported travel times did not represent the actual or perceived travel times in a truthful manner. The results of this study suggest that caution should be recommended when reported travel time data are used in an uncritical manner during transport research and when determining policy.     

Travel patterns in Dutch metropolitan cities: The importance of external trips

Using a variety of data sources, decentralisation of population and employment in four Dutch urban areas (Amsterdam, Rotterdam, The Hague and Utrecht) over the last 20 years, is analysed. It is found that suburbanisation, plus an ever growing car share, has increased the number of external journeys related to the metropolitan cities enormously. With the use of the 1982 National Travel Survey, current travel patterns in and around the metropolitan cities are studied: the importance of external trips for urban transport planning shows clearly. They account for about half the number of city related car trips, and for nearly three quarters of the total vehicle kilometres of travel within the city.(requests for reprints)     

Estimating origin‐destination matrices from link volume counts: a simple intercity auto travel demand model for Sri Lanka

The cost of nation wide travel surveys is high. Hence in many developing countries, planners have found it difficult to develop intercity transportation plans due to the non availability of origin‐destination trip matrices. This paper will describe a method for the intercity auto travel estimation for Sri Lanka with link traffic volume data.

The paper outlines the rationale of selecting the district capitals of Sri Lanka as its “cities,” the methodology for selecting the intercity road network, determination of link travel times from express bus schedules and the location of link volume counting positions.

Initially, the total auto travel demand model is formulated with various trip purpose sub‐models. This model is finally modified to a simple demand model with district urban population and travel times between city pairs as the exogenous variables, to overcome statistical estimation difficulties. The final demand model has statistics within the acceptable regions.

The advantages of a simple model are discussed and possible extensions are proposed.