10,670 research outputs found
A Multiclass Simulation-Based Dynamic Traffic Assignment Model for Mixed Traffic Flow of Connected and Autonomous Vehicles and Human-Driven Vehicles
One of the potential capabilities of Connected and Autonomous Vehicles (CAVs)
is that they can have different route choice behavior and driving behavior
compared to human Driven Vehicles (HDVs). This will lead to mixed traffic flow
with multiple classes of route choice behavior. Therefore, it is crucial to
solve the multiclass Traffic Assignment Problem (TAP) in mixed traffic of CAVs
and HDVs. Few studies have tried to solve this problem; however, most used
analytical solutions, which are challenging to implement in real and large
networks (especially in dynamic cases). Also, studies in implementing
simulation-based methods have not considered all of CAVs' potential
capabilities. On the other hand, several different (conflicting) assumptions
are made about the CAV's route choice behavior in these studies. So, providing
a tool that can solve the multiclass TAP of mixed traffic under different
assumptions can help researchers to understand the impacts of CAVs better. To
fill these gaps, this study provides an open-source solution framework of the
multiclass simulation-based traffic assignment problem for mixed traffic of
CAVs and HDVs. This model assumes that CAVs follow system optimal principles
with rerouting capability, while HDVs follow user equilibrium principles.
Moreover, this model can capture the impacts of CAVs on road capacity by
considering distinct driving behavioral models in both micro and meso scales
traffic simulation. This proposed model is tested in two case studies which
shows that as the penetration rate of CAVs increases, the total travel time of
all vehicles decreases
The development of river-based intermodal transport: the case of Ukraine
It should be noted that the (inland waterway transport) IWT in Ukraine currently is in its infancy in comparison with other land based transport means (rail and road) and with other countries that possess navigable rivers. This paper is an extension of the research initiated by Grushevska and Notteboom (2015) where the concepts of intermediacy and centrality were introduced in order to assess the role of Ukraine in the global and regional transport networks. The list of key obstacles for Ukraine’s intermediacy function included IWT related barriers such as: (i) deficient inland waterway infrastructure, (ii) high IWT costs (fees for bridges, locks etc.) and (iii) pilotage charges. To date the transportation to/from ports is mainly fulfilled by road or by rail based multimodal transport solutions. We present the unutilized potential of Ukrainian IWT that needs to be efficiently exploited for the benefit of the national economy and national transport system. This study intends to enrich the limited academic research on IWT systems in a transition stage, as exemplified by the case of Ukraine
The Voice of Optimization
We introduce the idea that using optimal classification trees (OCTs) and
optimal classification trees with-hyperplanes (OCT-Hs), interpretable machine
learning algorithms developed by Bertsimas and Dunn [2017, 2018], we are able
to obtain insight on the strategy behind the optimal solution in continuous and
mixed-integer convex optimization problem as a function of key parameters that
affect the problem. In this way, optimization is not a black box anymore.
Instead, we redefine optimization as a multiclass classification problem where
the predictor gives insights on the logic behind the optimal solution. In other
words, OCTs and OCT-Hs give optimization a voice. We show on several realistic
examples that the accuracy behind our method is in the 90%-100% range, while
even when the predictions are not correct, the degree of suboptimality or
infeasibility is very low. We compare optimal strategy predictions of OCTs and
OCT-Hs and feedforward neural networks (NNs) and conclude that the performance
of OCT-Hs and NNs is comparable. OCTs are somewhat weaker but often
competitive. Therefore, our approach provides a novel insightful understanding
of optimal strategies to solve a broad class of continuous and mixed-integer
optimization problems
Recommended from our members
Runway Operations Management: Models, Enhancements, and Decomposition Techniques
Air traffic loads have been on the rise over the last several decades and are expected to double, and possibly triple in some regions, over the coming decade. With the advent of larger aircraft and ever-increasing air traffic loads, aviation authorities are continually pressured to examine capacity expansions and to adopt better strategies for capacity utilization. However, this growth in air traffic volumes has not been accompanied by adequate capacity expansions in the air transport infrastructure. It is, therefore, predicted that flight delays costing multi-billion dollars will continue to negatively impact airline companies and consumers. In airport operations management, runways constitute a scarce resource and a key bottleneck that impacts system-wide capacity (Idris et al. 1999). Throughout the three essays that form this dissertation, enhanced optimization models and effective decomposition techniques are proposed for runway operations management, while taking into consideration safety and practical constraints that govern access to runways.
Essay One proposes a three-faceted approach for runway capacity management, based on the runway configuration, a chosen aircraft assignment/sequencing policy, and an aircraft separation standard as typically enforced by aviation authorities. With the objective of minimizing a fuel burn cost function, we propose optimization-based heuristics that are grounded in a classical mixed-integer programming formulation. By slightly altering the FCFS sequence, the proposed optimization-based heuristics not only preserve fairness among aircraft, but also consistently produce excellent (optimal or near optimal) solutions. Using real data and alternative runway settings, our computational study examines the transition from the (Old) Doha International Airport to the New Doha International Airport in light of our proposed optimization methodology.
Essay Two examines aircraft sequencing problems over multiple runways under mixed mode operations. To curtail the computational effort associated with classical mixed-integer formulations for aircraft sequencing problems, valid inequalities, pre-processing routines and symmetry-defeating hierarchical constraints are proposed. These enhancements yield computational savings over a base mixed-integer formulation when solved via branch-and-bound/cut techniques that are embedded in commercial optimization solvers such as CPLEX. To further enhance its computational tractability, the problem is alternatively reformulated as a set partitioning model (with a convexity constraint) that prompts the development of a specialized column generation approach. The latter is accelerated by incorporating several algorithmic features, including an interior point dual stabilization scheme (Rousseau et al. 2007), a complementary column generation routine (Ghoniem and Sherali, 2009), and a dynamic lower bounding feature. Empirical results using a set of computationally challenging simulated instances demonstrate the effectiveness and the relative merits of the strengthened mixed-integer formulation and the accelerated column generation approach.
Essay Three presents an effective dynamic programming algorithm for solving Elementary Shortest Path Problems with Resource Constraints (ESPPRC). This is particularly beneficial, because the ESPPRC structure arises in the column generation pricing sub-problem which, in turn, causes computational challenges as noted in Essay Two. Extending the work by Feillet et al. (2004), the proposed algorithm dynamically constructs optimal aircraft schedules based on the shortest path between operations while enforcing time-window restrictions and consecutive as well as nonconsecutive minimum separation times between aircraft. Using the aircraft separation standard by the Federal Aviation Administration (FAA), our computational study reports very promising results, whereby the proposed dynamic programming approach greatly outperforms the solution of the sub-problem as a mixed-integer programming formulation using commercial solvers such as CPLEX and paves the way for developing effective branch-and-price algorithms for multiple-runway aircraft sequencing problems
Assessing the impacts of COVID-19 on activity-travel scheduling: A survey in the greater Toronto area
The COVID-19 lockdown provided many individuals an opportunity to explore changes in their daily routines, particularly when considered in combination with an ever-changing Information and Communication Technology (ICT) landscape. These new routines and alternative activities have the potential to be continued in the post-COVID era. Transportation planners must understand how routines vary to effectively estimate activity-travel scheduling. The purpose of this study is to determine the influence of the COVID-19 pandemic lockdown on activity-travel behavior and the adoption of ICT-based alternative options. A special emphasis is placed on predicting the long-term effects of this disturbance on activity-travel scheduling. This study examines the changes in the frequency and mode of completing five of the most repetitious tasks in the daily schedule (working, grocery and non-grocery shopping, preparing/eating meals, and visiting family/friends) during the lockdown and immediately after reopening. We find an increased preference for home meal preparation over online ordering and a reluctance to engage in in-person shopping until a substantial proportion of the population has acquired a vaccination against the virus. Respondents prefer to work from home if they have adequate access to home office materials (e.g., desk, chair, computer monitor). Individuals with children must also consider suitable childcare before considering a return to work
- …