Design and analysis of demand‐adapted railway timetables

Abstract

Railway scheduling and timetabling are common stages in the classical hierarchical railway planning process and they perhaps represent the step with major influence on user's perception about quality of service. This aspect, in conjunction with their contribution to service profitability, makes them a widely studied topic in the literature, where nowadays many efforts are focused on improving the solving methods of the corresponding optimization problems. However, literature about models considering detailed descriptions of passenger demand is sparse. This paper tackles the problem of timetable determination by means of building and solving a non-linear integer programming model which fits the arrival and departure train times to a dynamic behavior of demand. The optimization model results are then used for computing several measures to characterize the quality of the obtained timetables considering jointly both user and company points of view. Some aspects are discussed, including the influence of train capacity and the validity of Random Incidence Theorem. An application to the C5 line of Madrid rapid transit system is presented. Different measures are analyzed in order to improve the insight into the proposed model and analyze in advance the influence of different objectives on the resulting timetable.

Influence of Real-Information to Change Tourists' Travel Route

“…Even though the relative weights of these three antecedents of intention are expected to vary with the kind of behavior being predicted and with the conditions under which the behavior is to be taken. A lot of literature have confirmed that attitude, subjective norms and perceived behavior control have strong, direct, and positive effect on intentions [10]. Hypothesis 1.…”

“…The theory of planned behavior (TPB) is a well-researched model which is widely used in explaining and in predicting human behavior across a variety of disciplines [10], such as marketing consumer behavior [13], leisure behavior [17], and disease prevention [24]. Thus, TPB proposes that three key constructs will drive behavior: attitude, subjective norms and perceived behavior control.…”

Analyzing the theoretical capacity of railway networks with a radial-backbone topology

“…These are based on the design of saturated schedules and use mathematical programming models that achieve a high degree of saturation and simultaneously ensure certain level of quality of service (Canca et al, 2014a). Different techniques have been used for this purpose: branch-and-bound (Szpigel, 1972;Jovanovic and Harker, 1991), heuristic algorithms (Cai and Goh, 1994;Carey and Lockwood, 1995), tabu search (Higgins et al, 1996), Lagrangian relaxation (Caprara et al, 2000), and linear programming (Mussone and Wolfter Calvo, 2013).…”

“…The vertical load factor VLF is 100%, corresponding to the section of higher occupancy (ratio 290/290). The load factor LF is HLF Á VLF ¼ 0:7331 (Canca et al, 2014a). Finally, the percentage of users who do not require transfers is 100%.…”

Modeling cascade dynamics of railway networks under inclement weather

“…On the other hand, it is also easy to transform periodic to non-periodic timetables using the unfolding technique, where the dependency between events in different periods is still reserved (Goverde, 2007;van der Meer, 2008). Additionally, partially periodic and non-periodic train services are also being adopted in practical train operations in Europe and China to cope with the non-uniformly distributed demand and exceptional situations, where the non-periodic expression would be applicable (Caimi et al, 2011;Forsgren et al, 2011;Wong et al, 2008;Canca et al, 2014;Cacchiani et al, 2014).…”