Optimization Of Headway,Vehicle Size and Route Choice for Minimum Cost Feeder Service

ABSTRACT

Many people use public transportation systems to reach their destination, while others use personal vehicles. Poor transportation systems do not attract ridership. Therefore, the usage of passenger cars increases, and traffic and environmental conditions deteriorate. Efficient public transportation has been recognized as one of the potential ways of mitigating air pollution, reducing energy consumption, improving mobility and alleviating traffic congestion. The objective of this study is to optimize a bus feeder service that provides the shuttle service between a recreation center (e.g. Sandy Hook, NJ) and a major public transportation facility, subject to site-specific constraints such as vehicle schedules, bus availability, service capacity and budget. The decision variables include bus headway, vehicle size and route choice. The solution methodology integrating both analytical and numerical techniques is developed, which optimizes the decision variables. Finally, the proposed solution methodology is applied to a case study. Numerical results, including optimal solutions and sensitivity analyses, are presented while the level of coordination between the feeder service and a major transportation service is discussed.     

The value of service reliability

This paper studies the impact of service frequency and reliability on the choice of departure time and the travel cost of transit users. When the user has (α, β, γ) scheduling preferences, we show that the optimal head start decreases with service reliability, as expected. It does not necessarily decrease with service frequency, however. We derive the value of service headway (VoSH) and the value of service reliability (VoSR), which measure the marginal effect on the expected travel cost of a change in the mean and in the standard deviation of headways, respectively. The VoSH and the VoSR complete the value of time and the value of reliability for the economic appraisal of public transit projects by capturing the specific link between headways, waiting times, and congestion. An empirical illustration is provided, which considers two mass transit lines located in the Paris area.     

A methodology to control urban traffic noise under the constraint of environmental capacity: A case study of a double-decision optimization model

This paper presents a novel methodology to control urban traffic noise under the constraint of environmental capacity. Considering the upper limits of noise control zones as the major bottleneck to control the maximum traffic flow is a new idea. The urban road network traffic is the mutual or joint behavior of public self-selection and management decisions, so is a typical double decision optimization problem.The proposed methodology incorporates theoretically model specifications. Traffic noise calculation model and traffic assignment model for O–D matrix are integrated based on bi-level programming method which follows an iterated process to obtain the optimal solution. The upper level resolves the question of how to sustain the maximum traffic flow with noise capacity threshold in a feasible road network. The user equilibrium method is adopted in the lower layer to resolve the O–D traffic assignment.The methodology has been applied to study area of QingDao, China. In this illustrative case, the noise pollution level values of optimal solution could satisfy the urban environmental noise capacity constraints. Moreover, the optimal solution was intelligently adjusted rather than simply reducing the value below a certain threshold. The results indicate that the proposed methodology is feasible and effective, and it can provide a reference for a sustainable development and noise control management of the urban traffic.     

Optimization of Fare Structure and Service Frequency for Maximum Profitability of Transit Systems

Abstract

Providing efficient public transportation has been recognized as a potential way of alleviating congestion, improving mobility, mitigating air pollution, and reducing energy consumption. Many people use public transportation systems for their daily commute, while others use different transportation modes (e.g. cars, taxis, carpools, etc.). Inexpensive fares with good transit service encourages ridership, and the resulting revenue may be used to provide better service. Optimization of transit service frequency and its associated fare structure is desirable in order to increase revenue at reasonable transit operating expenditure. The objective of the study reported here is to maximize profit subject to service capacity constraint, while elastic demand is considered. The solution methodology is developed and applied to solve the profit maximization problem in a case study based on Newark, NJ, USA. Numerical results, including optimal solutions and sensitivity analyses, are presented. It is found that an optimal temporal headway and differential fare structure that maximizes total profit for the studied subway system can be efficiently solved.     

Nature and/or nurture? Analyzing the determinants of transit ridership across US urbanized areas

Public subsidy of transit services has increased dramatically in recent years, with little effect on overall ridership. Quite obviously, a clear understanding of the factors influencing transit ridership is central to decisions on investments in and the pricing and deployment of transit services. Yet the literature about the causes of transit use is quite spotty; most previous aggregate analyses of transit ridership have examined just one or a few systems, have not included many of the external, control variables thought to influence transit use, and have not addressed the simultaneous relationship between transit service supply and consumption. This study addresses each of these shortcomings by (1) conducting a cross-sectional analysis of transit use in 265 US urbanized areas, (2) testing dozens of variables measuring regional geography, metropolitan economy, population characteristics, auto/highway system characteristics, and transit system characteristics, and (3) constructing two-stage simultaneous equation regression models to account for simultaneity between transit service supply and consumption. We find that most of the variation in transit ridership among urbanized areas – in both absolute and relative terms – can be explained by factors outside of the control of public transit systems: (1) regional geography (specifically, area of urbanization, population, population density, and regional location in the US), (2) metropolitan economy (specifically, personal/household income), (3) population characteristics (specifically, the percent college students, recent immigrants, and Democratic voters in the population), and (4) auto/highway system characteristics (specifically, the percent carless households and non-transit/non-SOV trips, including commuting via carpools, walking, biking, etc.). While these external factors clearly go a long way toward determining the overall level of transit use in an urbanized area, we find that transit policies do make a significant difference. The observed range in both fares and service frequency in our sample could account for at least a doubling (or halving) of transit use in a given urbanized area. Controlling for the fact that public transit use is strongly correlated with urbanized area size, about 26% of the observed variance in per capita transit patronage across US urbanized areas is explained in the models presented here by service frequency and fare levels. The observed influence of these two factors is consistent with both the literature and intuition: frequent service draws passengers, and high fares drive them away.     

Game-theoretical models for competition analysis in a new emerging liner container shipping market

This paper develops three game-theoretical models to analyze shipping competition between two carriers in a new emerging liner container shipping market. The behavior of each carrier is characterized by an optimization model with the objective to maximize his payoff by setting optimal freight rate and shipping deployment (a combination of service frequency and ship capacity setting). The market share for each carrier is determined by the Logit-based discrete choice model. Three competitive game strategic interactions are further investigated, namely, Nash game, Stackelberg game and deterrence by taking account of the economies of scale of the ship capacity settings. Three corresponding competition models with discrete pure strategy are formulated as the variables in shipment deployment are indivisible and the pricing adjustment is step-wise in practice. A ɛ -approximate equilibrium and related numerical solution algorithm are proposed to analyze the effect of Nash equilibrium. Finally, the developed models are numerically evaluated by a case study. The case study shows that, with increasing container demand in the market, expanding ship capacity setting is preferable due to its low marginal cost. Furthermore, Stackelberg equilibrium is a prevailing strategy in most market situations since it makes players attain more benefits from the accommodating market. Moreover, the deterrence effects largely depend on the deterrence objective. An aggressive deterrence strategy may make potential monopolist suffer large benefit loss and an easing strategy has little deterrence effect.     

Demand-driven timetable design for metro services

Timetable design is crucial to the metro service reliability. A straightforward and commonly adopted strategy in daily operation is a peak/off-peak-based schedule. However, such a strategy may fail to meet dynamic temporal passenger demand, resulting in long passenger waiting time at platforms and over-crowding in trains. Thanks to the emergence of smart card-based automated fare collection systems, we can now better quantify spatial–temporal demand on a microscopic level. In this paper, we formulate three optimization models to design demand-sensitive timetables by demonstrating train operation using equivalent time (interval). The first model aims at making the timetable more dynamic; the second model is an extension allowing for capacity constraints. The third model aims at designing a capacitated demand-sensitive peak/off-peak timetable. We assessed the performance of these three models and conducted sensitivity analyzes on different parameters on a metro line in Singapore, finding that dynamical timetable built with capacity constraints is most advantageous. Finally, we conclude our study and discuss the implications of the three models: the capacitated model provides a timetable which shows best performance under fixed capacity constraints, while the uncapacitated model may offer optimal temporal train configuration. Although we imposed capacity constraints when designing the optimal peak/off-peak timetable, its performance is not as good as models with dynamical headways. However, it shows advantages such as being easier to operate and more understandable to the passengers.     

An Analysis of the Dynamics of Ownership,Capacity Investments and Pricing Structure of Ports

With more than 80% of the world's cargoes being transported by sea, effective port management is critical to the well-being of the global economy. This study models the effects of port ownership and governance on capacity investment and pricing structure, and these changes' implications on port service level and social welfare. The study argues that capacity investment and pricing are significantly influenced by a port's ownership form, and the different levels of government involved. Inter-port competition leads to increased capacity investments by private investors and local authorities, which can be either higher or lower than social optimal level. Therefore, it is important for policymakers to consider the effects of institutional and competition factors in port reform initiatives.     

Dynamic stability metrics for the container loading problem

The Container Loading Problem (CLP) literature has traditionally evaluated the dynamic stability of cargo by applying two metrics to box arrangements: the mean number of boxes supporting the items excluding those placed directly on the floor (M1) and the percentage of boxes with insufficient lateral support (M2). However, these metrics, that aim to be proxies for cargo stability during transportation, fail to translate real-world cargo conditions of dynamic stability.In this paper two new performance indicators are proposed to evaluate the dynamic stability of cargo arrangements: the number of fallen boxes (NFB) and the number of boxes within the Damage Boundary Curve fragility test (NB_DBC). Using 1500 solutions for well-known problem instances found in the literature, these new performance indicators are evaluated using a physics simulation tool (StableCargo), replacing the real-world transportation by a truck with a simulation of the dynamic behaviour of container loading arrangements.Two new dynamic stability metrics that can be integrated within any container loading algorithm are also proposed. The metrics are analytical models of the proposed stability performance indicators, computed by multiple linear regression. Pearson’s r correlation coefficient was used as an evaluation parameter for the performance of the models. The extensive computational results show that the proposed metrics are better proxies for dynamic stability in the CLP than the previous widely used metrics.     

新时期“服务区+旅游”发展模式及功能定位

为了满足人们对高速公路服务区功能多元化需求,支撑高速公路服务区可持续发展,本文探索了“服务区+旅游”概念,从地理位置、自然资源、文化资源、产业资源、车流量多维度分析了“服务区+旅游”特征,研究了新时期背景下“服务区+旅游”典型融合模式和单体服务区+旅游融合发展功能定位,并以莆炎高速(福州段)梧桐服务区为开展实证研究。结果表明:1)服务区+旅游典型模式宜划分为综合型、自然资源型、人文资源型、基本型四种;2)单体服务区+旅游融合发展应从区位因素、资源因素、经济因素进行功能定位;3)通过实践应用,福建莆炎高速梧桐服务区功能定位为综合型服务区。研究成果可以强化服务区社会效益,适当提高其经济效益,支撑高速公路服务区的可持续发展。     

Optimization of terminal airspace operation with environmental considerations

The rapid growth in air traffic has resulted in increased emission and noise levels in terminal areas, which brings negative environmental impact to surrounding areas. This study aims to optimize terminal area operations by taking into account environmental constraints pertaining to emission and noise. A multi-objective terminal area resource allocation problem is formulated by employing the arrival fix allocation (AFA) problem, while minimizing aircraft holding time, emission, and noise. The NSGA-II algorithm is employed to find the optimal assignment of terminal fixes with given demand input and environmental considerations, by incorporating the continuous descent approach (CDA). A case study of the Shanghai terminal area yields the following results: (1) Compared with existing arrival fix locations and the first-come-first-serve (FCFS) strategy, the AFA reduces emissions by 19.6%, and the areas impacted by noise by 16.4%. AFA and CDA combined reduce the emissions by 28% and noise by 38.1%; (2) Flight delays caused by the imbalance of demand and supply can be reduced by 72% (AFA) and 81% (AFA and CDA) respectively, compared with the FCFS strategy. The study demonstrates the feasibility of the proposed optimization framework to reduce the environmental impact in terminal areas while improving the operational efficiency, as well as its potential to underpin sustainable air traffic management.     

Integrated scheduling of m-truck,m-drone,and m-depot constrained by time-window,drop-pickup,and m-visit using constraint programming

The idea of deploying unmanned aerial vehicles, also known as drones, for final-mile delivery in logistics operations has vitalized this new research stream. One conceivable scenario of using a drone in conjunction with a traditional delivery truck to distribute parcels is discussed in earlier literature and termed the parallel drone scheduling traveling salesman problem (PDSTSP). This study extends the problem by considering two different types of drone tasks: drop and pickup. After a drone completes a drop, the drone can either fly back to depot to deliver the next parcels or fly directly to another customer for pickup. Integrated scheduling of multiple depots hosting a fleet of trucks and a fleet of drones is further studied to achieve an operational excellence. A vehicle that travels near the boundary of the coverage area might be more effective to serve customers that belong to the neighboring depot. This problem is uniquely modeled as an unrelated parallel machine scheduling with sequence dependent setup, precedence-relationship, and reentrant, which gives us a framework to effectively consider those operational challenges. A constraint programming approach is proposed and tested with problem instances of m-truck, m-drone, m-depot, and hundred-customer distributed across an 8-mile square region.     

Promoting Public Transport Using Marketing Techniques in Mobility Management and Verifying their Quantitative Effects

Mobility management (MM) is a transportation management policy that uses “soft” measures to attempt to reduce car use and promote sustainable transportation modes such as public transport, bicycles, and walking. Using communication and other means, MM induces voluntarily change towards more sustainable transportation modes. We implemented MM marketing to promote an experimental community bus service. This project had two components: a questionnaire conducted in the service area and a monthly newsletter. The questionnaire was more than a survey; it also communicated information about the bus and helped promote bus use. One month after the survey, we implemented a follow-up survey targeting the initial survey respondents. Results suggest that the MM program produced a general increase in bus use, as well as mouth-to-mouth advertising, that helped promote bus use.     

The impact of transit station areas on the travel behaviors of workers in Denver,Colorado

Transit development is one planning strategy that seeks to partially overcome limitations of low-density single use car oriented development styles. While many studies focus on how residential proximity to transit influences the travel behaviors of individuals, the effect of workplace proximity to transit is less understood. This paper asks, does working near a light rail transit station influence the travel behaviors of workers differently than workers living near a station? We begin by examining workers’ commute mode based on their residential and workplace proximity to transit station areas. Next, we analyze the ways in which personal travel behaviors differ between those who drive to work and those who do not. The data came from a 2009 travel behavior survey in the Denver, Colorado metropolitan area, which contains 8000 households, 16,000 individuals, and nearly 80,000 trips. We measure sustainable travel behaviors as reduced mileage, reduced number of trips, and increased use of non-car transportation. The results of this study indicate that living near a transit station area by itself does not increase the likelihood of using non-car modes for work commutes. But if the destination (work) is near a transit station area, persons are less likely to drive a car to work. People who both live and work in a transit station area are less likely to use a car and more likely to take non-car modes for both work and non-work (personal) trips. Especially for persons who work near a transit station area, the measures of personal trips and distances show a higher level of mobility for non-car commuters than car commuters – that is, more trips and more distant trips. The use of non-car modes for personal trips is most likely to occur by non-car commuters, regardless of their transit station area relationship.     

An approximate analytic model of supertanker lightering operations

The most economical means of transporting crude oil over long distances is through the use of very large and ultra large crude containers (VLCCs and ULCCs respectively). These ships require sea lane depths of more than 55 feet to navigate. Since no major U.S. port is deeper than 45 feet, special offloading procedures must be employed. One such procedure is lightering, the process of offloading crude oil from VLCCs to smaller vessels for final delivery to the port.A linked set of queuing models is developed in an effort to understand lightering operations better and to enhance tradeoff analyses. The model assumes that port service times, lightering vessel loading times and VLCC interarrival times are exponentially distributed, allowing us to model lightering vessel operations using a cyclic queuing model. VLCC delays are modeled using an approximate M/E_k/S queue. The two models are linked through a VLCC service time model. The sensitivity of the model to changes in key inputs as well as the likely impacts of the model's assumptions and approximations are discussed. The paper concludes with recommendations for future studies.     

Approximate queueing models for analyzing harbor terminal operations

The purpose of this paper is to present approximate queueing models to help assess the impacts of tug services on congested harbor terminals. The models are intended for harbors in which tug shortages are rare. A congested harbor terminal is modelled as a queueing system with m identical tugs (servers) and n identical berths (customers), and with general probability distributions of tug service time and berth cargo-handling time. The distribution of the number of berths in the system, a basic element to analyze the system performance, was established for two cases. For large m, the distribution was approximated by a binomial model and the respective accuracy tested. For small m, an approximate model for this distribution was developed. Particular emphasis was given to developing explicitly the probability of having one berth in queue and establishing the remaining probabilities of the distribution approximately. The model for small m was validated by means of simulation for various cases of harbor terminal operations exhibiting different ranges of the coefficient of variation of tug service time. The models were found to be reasonably accurate within a certain range covering situations in which tug shortages are in the order of 10% of the time or less.     

Immigration, residential location, car ownership, and commuting behavior: a multivariate latent class analysis from California

Using latent class cluster analysis, this paper investigates the spatial, social, demographic, and economic determinants of immigrants’ joint distribution among travel time, mode choice, and departure time for work using the 2000 Census long form data. Through a latent tree structure analysis, age, residential location, immigration stage, gender, personal income, and race are found to be the primary determinants in the workplace commute decision-making process. By defining several relatively homogeneous population segments, the likelihood of falling into each segment is found to differ across age groups and geography, with different indicators affecting each group differentially. This analysis complements past studies that used regression models to investigate socio-demographic indicators and their impact on travel behavior in two distinct ways: (a) analysis is done by considering travel time, mode choice, and departure time for work simultaneously, and (b) heterogeneity in behavior is accounted for using methods that identify different groups of behavior and then their determinants. Conclusively the method here is richer than many other methods used to study the ethnically diverse population of California and shows the addition of geographic location and latent segment identification to greatly improve our understanding of specific behaviors. It also provides evidence that immigrants are as diverse as the non-immigrant population and transportation policies need to be defined accordingly.

Incorporating institutional and spatial factors in the selection of the optimal locations of public electric vehicle charging facilities: A case study of Beijing,China

In this paper, we present a case study on planning the locations of public electric vehicle (EV) charging stations in Beijing, China. Our objectives are to incorporate the local constraints of supply and demand on public EV charging stations into facility location models and to compare the optimal locations from three different location models. On the supply side, we analyse the institutional and spatial constraints in public charging infrastructure construction to select the potential sites. On the demand side, interviews with stakeholders are conducted and the ranking-type Delphi method is used when estimating the EV demand with aggregate data from municipal statistical yearbooks and the national census. With the estimated EV demand, we compare three classic facility location models – the set covering model, the maximal covering location model, and the p-median model – and we aim to provide policy-makers with a comprehensive analysis to better understand the effectiveness of these traditional models for locating EV charging facilities. Our results show that the p-median solutions are more effective than the other two models in the sense that the charging stations are closer to the communities with higher EV demand, and, therefore, the majority of EV users have more convenient access to the charging facilities. From the experiments of comparing only the p-median and the maximal covering location models, our results suggest that (1) the p-median model outperforms the maximal covering location model in terms of satisfying the other’s objective, and (2) when the number of charging stations to be built is large, or when minor change is required, the solutions to both models are more stable as p increases.     

A continuum approximation approach to competitive facility location design under facility disruption risks

This paper presents game-theoretical models based on a continuous approximation (CA) scheme to optimize service facility location design under spatial competition and facility disruption risks. The share of customer demand in a market depends on the functionality of service facilities and the presence of nearby competitors, as customers normally seek the nearest functioning facility for service. Our game-theoretical models incorporate these complicating factors into an integrated framework, and use continuous and differentiable density functions to represent discrete location decisions. We first analyze the existence of Nash equilibria in a symmetric two-company competition case. Then we build a leader–follower Stackelberg competition model to derive the optimal facility location design when one of the companies has the first mover advantage over its competitor. Both models are solved effectively, and closed-form analytical solutions can be obtained for special cases. Numerical experiments (with hypothetical and empirical data) are conducted to show the impacts of competition, facility disruption risks and transportation cost metrics on the optimal design. Properties of the models are analyzed to cast interesting managerial insights.     

Physical distribution from a warehouse: Vehicle coverage and inventory levels

This paper studies the costs involved in distributing items from a warehouse or depot to randomly scattered customers on a day-to-day basis. Two trade-offs are explored simultaneously. The first one arises because by accumulating large inventories at the depot it is possible to build more efficient distribution tours. This trade-off has already been explored for both distribution of goods (Burns et al., 1983) and passengers (Daganzo et al., 1977; Hendrickson, 1978). Another tradeoff, which involves the length of individual vehicle tours (Clarens and Hurdle, 1975), balances the inventory inside the vehicles against the transportation cost. Banks et al. (1982) have considered both of these tradeoffs simultaneously in the context of passenger transportation, but used a somewhat unrealistic model for vehicle routing. This paper is similar to the latter reference but uses a different routing strategy. It also illustrates how the nature of the objects carried (cheap goods, expensive goods, people, etc.) affects the optimal configuration of the distribution system and the overall distribution costs. Usually there is an optimum partitioning of the service area into districts and an optimum dispatching frequency in each district. The results can vary tremendously, depending on factors such as: the inventory carrying cost per item per unit time, the transportation costs, the demand per unit area and unit time, the average distance from the depot, the average vehicle speed and the time per stop.As an illustration of the ideas, a hypothetical limousine service from an airport is analyzed. The example is used to demonstrate how dramatically the optimal system configuration depends on the nature of the items carried.