This research provides insight into ways to increase accessibility for Planned Special Events (PSEs) via discrete choice modelling. A special focus within this study is put into the AFAS AZ Stadium in Alkmaar. During the events held at the AFAS AZ Stadium disturbances are experienced by its visitors with regard to crowding levels and access/egress times. Quantifying the traffic volume on the infrastructure around the venue is done via revealed preference methods. Cyclists are counted via pneumatic tubes. Whereas pedestrians are measured via radio-wave sensors. To find ways to mitigate these problems, interviews are held with organisers of similar PSEs within The Netherlands. To find the effectiveness of the obtained accessibility measures a stated preference study is held. From this study it is obtained that the transportation mode habit that one has is a key factor within the mode choice. Besides the effect on accessibility additional decision factors are discussed to find the best measure which can be applied best at the AFAS AZ Stadium. Based on the decision factors and the performance of the accessibility measures a total of six effective measures are determined.

Cycling has gained popularity worldwide, but little is known about the choice behaviour of people searching for a bicycle parking spot in the city centre. Formal parking spaces provided in the city centre are currently often underutilised, whilst many people use fly parking, which causes nuisance. This thesis aims to identify the most effective communication strategy to reduce the utilisation of fly parking and whether this differs for distinct user groups. Traffic signs were chosen as a means to present the different communication strategies. Information on choice behaviour was gathered with the use of a stated preference survey. Discrete choice modelling was used to analyse this data. The results show communication prohibiting the use of fly parking work very effectively in reducing fly parking. Interactions of socio-demographics and communication strategies were also found, indicating that belonging to a certain user group affects the effectiveness of the different communication strategies. What should be noted is that the effect of the interactions is marginal compared to the effect of the communication strategy itself. Meaning that the effectiveness of the communication strategy itself is most important. The research also tested how well traffic signs performed as a means of delivering the communication. It was concluded that solely showing traffic signs without providing any prior or other information is not very effective.

Nowadays, urban areas are exposed to various challenges such as climate change, social inequalities, and congestion. Mobility hubs present the opportunity to reshape our cities and mitigate the previously mentioned problems by contributing to a more sustainable and equitable transport system. This thesis defines mobility hubs as places where shared cars, mopeds, and e-bikes are offered to improve connectivity and ameliorate mobility options. Given a limited budget, cities would like to optimize the locations of mobility hubs to maximize benefits. This problem is solved in this thesis by presenting an optimization model that allows the distribution of mobility hubs and allocation of shared cars, mopeds, and e-bikes to maximize social welfare. The algorithm can provide the optimal locations for the hubs and their respective capacity in terms of vehicles, while accounting for multimodal trips. It focuses on maximizing the utility of the population rather than the operators’ profits. The model is divided into several modules: computational modules that calculate the number of people that would like to use a mobility hub; a mathematical optimization module to optimize the capacity, availability, and relocation of shared vehicles; and finally, a genetic algorithm that performs several iterations to find the optimal distribution of hubs. The model developed is applied in a case study for the city of Amsterdam. Several scenarios are performed to assess how the distribution of hubs varies depending on the budget provided to construct them. The results show that having more hubs with a lower number of shared vehicles is more beneficial than having fewer with more vehicles. Areas with higher population density are prioritized when lower budgets are invested in building the hubs. Additionally, the shift towards shared modes and the travel time savings are minimal. The benefits increase considerably when investments lead to complete coverage of the area by the network of mobility hubs. A modal split of 5% for the shared modes is expected when Amsterdam is fully covered by 288 hubs. From an environmental point of view, only 32 % of the shared trips replace trips previously made by car, leading to a limited CO2 emissions reduction of 1.27%. To conclude, the model developed is one of the first models that optimizes the location and capacity of multimodal hubs to maximize social welfare by considering multimodal trips. It has the ability to quantify the benefits of introducing mobility hubs depending on the investments made.

Efficient repositioning strategies for idle vehicles in ride-sourcing systems help reduce passengers' waiting time and drivers' operational costs, which help platforms attract more passengers and drivers. In this paper, we propose a decentralized repositioning strategy for drivers, in which drivers make individual decisions on where they reposition themselves, based on their own experiences. In comparison to an existing centralized repositioning strategy, in which drivers comply with reposition instructions provided by the platform, we examine the effects of the decentralized strategy on service rate, passengers' waiting time and drivers' net income. We compare the reposition strategies under different supply and demand levels and different demand spatial distribution dispersion rates. We also explore the influence of platform information on drivers' decision-making process. We found that decentralized repositioning strategies have better performance in reducing waiting time, while the centralized strategy is better at increasing driver income and service rate. We also found that when platform information is accessible, the system has the best performance when 20% to 60% proportion of drivers utilize platform information when making decisions.

In the Netherlands, online groceries are becoming increasingly popular, as are the challenges grocery companies face in meeting customers' rising demand for smaller and cheaper time slots while maintaining thin profit margins due to a highly competitive market. Customer choice modelling will be used to identify customers' behaviour and control the trade-offs between customer attractiveness and profitability with demand management. As there are parametric and non-parametric models, including Machine Learning, to identify the behaviour, they will be compared to define which model represents customer behaviour best. Based on the F1-score, it is evaluated that the Multinomial logit (MNL) and Neural Network (NN) models outperform the created Benchmark model, derived from assumptions from the data, to ensure added value in predicting performance. Since it is found that the advanced predictive models can provide a better understanding of customer behaviour and identify critical customer characteristics, it will be tested whether these models enhance the simulation closer to reality or produce similar results to the Benchmark model, and if so, whether they can be used to optimise the offer strategy. In essence, does the learned behaviour impact the routes and number of offered slots, and can it be utilised to optimise the provided set of offers? A simulation tool is conducted to determine this, which uses real-selected customer time slots to assess any differences from reality and collect key performance indicators (KPIs) for evaluation. The results of the simulations show that incorporating advanced choice models in the simulations adds value and brings the outcomes closer to reality. As a result, a first attempt is made with two test scenarios to determine whether the advanced choice models can be used to optimise the offer strategy. Promising outcomes are found by analysing the results. However, further research is needed to assess the exact impact of the strategies. In future research, it is recommended to determine the influence of using more historical data and data from other companies in combination with other choice models. Also, it is recommended to consider different strategies for optimising the offer set based on incentives or other features.

In the airline industry revenue and inventory is managed tightly, with the intent to sell each seat at the right price to the right customer at the right time. This does not always go to plan and seats remain unsold, or demand can be greater than what is available. The question then becomes how can capacity and revenue be managed more efficiently? How can capacity and revenue be maximized to ensure that seat spoilage is minimized. This thesis takes a look at this problem and aims to solve this problem by dynamically allocating capacity as well as becoming paired with revenue management processes. By being able to move capacity around to different flights, it is believed that revenue can be maximized and that seat spoilage can be minimized, creating a more effective use of a perishable product. The goal of this thesis is to show that increases are possible, even if by small margins. This thesis looks to use real-world data to help support its findings along with a random distribution to pair alongside a Monte Carlo simulation to help ensure accuracy. In this data driven methodological approach revenue management and a capacity swapping algorithm are integrated. The revenue management module creates a series of seat inventory allocations as well as demand forecasting. This information is then fed into a simulation which will then simulate the entire booking life- cycle of a fight. Here each flight will build its passenger load and will swap aircraft with other aircraft of varying capacities when the algorithm has determined it is necessary and would create a positive increase in revenue and passenger counts. To assist in this approach a further random forest model is developed to help make informed decisions. These changes are further evaluated by the algorithm to ensure that every change that was made was beneficial and if so will accept the swap and continue to build loads as this new flight. Different case studies were developed that introduced different timing scenarios as well as different decision making tools. The two different decision making tools would use either the random forest model that was developed or a simple logic model that would look at estimated final revenue and estimated final passenger loads. The timing scenarios would look to see when swapping would be needed, whether it is a true daily swap or if swaps can be performed once a month. Ultimately there were positive results, with each scenario tested yielding positive increases in the three KPIs examined (Load Factor, Passengers, Revenue). Each scenario involved testing either the simple logic, or random forest model with varying swapping intervals. The best performing scenario was one where flights were allowed to be swapped daily using a random forest decision model. In this best scenario load factor saw an increase of 1%, the number of passengers increased 1.4%, and revenue increased 0.9%; all against the baseline scenario where zero flights were swapped. This translates into a 36,000 USD weekly increase for a small sample in a route network. Across a full route network, even greater increases can be achieved.

The simultaneous rapidly increasing demand for home delivery of goods and on-demand expectancy of customers over the past years leaves a tough challenge for the logistical branch. They have to keep up with this increasing demand and simultaneously they are obliged to satisfy consumer service level demands to preserve their customers. On the other hand, as economic goals drive these businesses, they are prompted to operate cost-effectively. As a result, the fleet, deployed to execute the last-mile delivery, should meet both the requirement of cost-efficiency as well as the requirement for meeting consumer service level demands. This raises the question of how to efficiently design a fleet for last-mile on-demand logistics. For a fleet to be able to operate cost-efficiently, the fleet design decisions are required to take both fixed and variable costs into account. As such, the fleet design decisions need to include the consideration of the size of the fleet as well as the distance the vehicles travel on daily basis. Therefore, the goal of this thesis is to develop a novel method for fleet design for last-mile on-demand logistics. This work contributes by being the first to investigate methods for doing fleet design specifically for last-mile on-demand logistics considering multiple depots and variable pick-up locations. The purpose of the method is to determine the operational plans of the individual vehicles, the number of vehicles needed throughout a certain time period, the pick-up locations for all orders and the total distance travelled by the full fleet of vehicles. The proposed method builds upon established fleet design methods for ride-sharing taxi problems. The optimization method is adapted for last-mile on-demand logistics, yielding the required number of vehicles and their individual operational plans. The input of the system is a set of trips, which represent a path of a single vehicle to deliver one or multiple orders from a depot. Connecting two trips, which is called chaining, has the benefit of reducing the number of vehicles used, as chained trips are served by a single vehicle. Additionally, from multiple available depots where orders can be picked up, the method determines the best depot per order. This part of the method is called depot re-assignment. Furthermore, the fleet design problem is modelled as a multi-objective optimisation problem to find the trade-off between fleet size and the total distance the vehicles travel. Three different modelled datasets, each containing 10.000 order requests in the city centre of Amsterdam, are used to prove the value of the given method. A comparison between the method with and without depot re-assignment is made, to prove the value of the given addition of depot re-assignment. It is proven that depot re-assignment is valuable as it decreases or retains the fleet size for all test cases. The experiments conducted show that a significant decrease of the required fleet size can be established by a minor increase in total travelled distance. Furthermore, the optimal trade-off between the fleet size and the total distance travelled can be determined for a specific operation with the knowledge of operational costs for that operation.

Electric vehicles (EVs) take advantage of reducing fossil-based environmental pollution and developing a more sustainable logistics network. Compared with internal combustion engine vehicles (ICEVs), EVs have new technical characteristics like limited battery capacity and long charging time at charging stations. With the diffusion of EVs, vehicle routing problems (VRPs) of EVs draw transportation service providers’ attention, which extends VRPs with intra-route charging considerations. In real-life practice, the punctuality of preplanned vehicle routing may be affected by uncertain travel time caused by traffic congestion, which derives undesirable penalty costs for violating time windows. Besides, long intra-route charging time at charging stations presents an even greater challenge to trade-off between completing tours with enough electricity and providing delivery service on time. This assignment aims to investigate the impact of travel time uncertainty and electric vehicle characteristics on planning fleet size, vehicle routing, and charging schedules. A pickup and delivery problem of electric vehicles is studied in this assignment, which considered flexible fleet size, partial charging policy, soft time windows and uncertain travel time. The problem is formulated as a two-stage stochastic linear programming model. The fleet size, vehicle routing, and charging decisions are determined in the first stage. After the realization of travel times, the second stage determines specific charging times at charging stations. The objective is to minimize the total operational cost, which consists of travel costs of vehicle usage and charging, and the expected penalty cost of earliness, delay and overtime. The sample average approximation method is applied to model the stochastic programming to the deterministic equivalent and the Gurobi Optimizer is used to solve this mixed-integer programming. A computational experiment is conducted based on a small data set with one depot, 9 pickup and delivery requests, 5 charging stations at service vertices and 3 available electric trucks. The travel times along the delivery tour are assumed to follow Gamma distribution. To investigate the trade-off between travel costs and penalty costs, 12 instances are conducted in the experiment by tuning parameters of time windows, the uncertainty of travel time, and the importance of punctuality. The experiment results showed an increasing vehicle fleet size could improve the level of service but also derive more vehicle usage costs. Besides, the intra-route charging operations impact vehicles’ departure times at each charging station, which subsequently impacts service start time at customer vertices. Longer intra-route charging time has the potential to enhance punctuality in instances with tight time windows or congested traffic conditions. Moreover, different operator inclination leads to different fleet size and charging time preference. Instances attaching more importance to punctuality have a larger fleet size and longer intra-route charging time.