Estimation of a constrained multinomial logit model

Identifying the set of available alternatives in a choice process after considering an individual’s bounds or thresholds is a complex process that, in practice, is commonly simplified by assuming exogenous rules in the choice set formation. The Constrained Multinomial Logit (CMNL) model incorporates thresholds in several attributes as a key endogenous process to define the alternatives choice/rejection mechanism. The model allows for the inclusion of multiple constraints and has a closed form. In this paper, we study the estimation of the CMNL model using the maximum likelihood function, develop a methodology to estimate the model overcoming identification problems by an endogenous partition of the sample, and test the model estimation with both synthetic and real data. The CMNL model appears to be suitable for general applications as it presents a significantly better fit than the MNL model under constrained behaviour and replicates the MNL estimates in the unconstrained case. Using mode choice real data, we found significant differences in the values of times and elasticities between compensatory MNL and semi-compensatory CMNL models, which increase as the thresholds on attributes become active.     

Modeling and optimization of multimodal urban networks with limited parking and dynamic pricing

Cruising-for-parking constraints mobility in urban networks. Car-users may have to cruise for on-street parking before reaching their destinations. The accessibility and the cost of parking significantly influence people's travel behavior (such as mode choice, or parking facility choice between on-street and garage). The cruising flow causes delays eventually to everyone, even users with destinations outside limited parking areas. It is therefore important to understand the impact of parking limitation on mobility, and to identify efficient parking policies for travel cost reduction. Most existing studies on parking fall short in reproducing the dynamic spatiotemporal features of traffic congestion in general, lack the treatment of dynamics of the cruising-for-parking phenomenon, or require detailed input data that are typically costly and difficult to collect. In this paper, we propose an aggregated and dynamic approach for modeling multimodal traffic with the treatment on parking, and utilize the approach to design dynamic parking pricing strategies. The proposed approach is based on the Macroscopic Fundamental Diagram (MFD), which can capture congestion dynamics at network-level for single-mode and bi-modal (car and bus) systems. A parsimonious parking model is integrated into the MFD-based multimodal modeling framework, where the dynamics of vehicular and passenger flows are considered with a change in the aggregated behavior (e.g. mode choice and parking facility choice) caused by cruising and congestion. Pricing strategies are developed with the objective of reducing congestion, as well as lowering the total travel cost of all users. A case study is carried out for a bi-modal city network with a congested downtown region. An elegant feedback dynamic parking pricing strategy can effectively reduce travel delay of cruising and the generic congestion. Remarkably, such strategy, which is applicable in real-time management with limited available data, is fairly as efficient as a dynamic pricing scheme obtained from system optimum conditions and a global optimization with full information about the future states of the system. Stackelberg equilibrium is also investigated in a competitive behavior between different parking facility operators. Policy indications on on-street storage capacity management and pricing are provided.     

共享单车停放需求影响因素分析和预测研究

为了解决城市共享单车的乱停乱放问题,本文基于北京市的共享单车出行大数据,提出了共享单车停放需求预测的多项Logit模型。首先分析了单车停放需求的影响因素,然后选取了时间、空间及天气方面的12个因素为自变量,通过Wald检验分析了这些因素与停放需求的相关性和显著性,基于多项Logit模型建立了共享单车的停放需求预测模型。结果表明:工作日、时段、商业区、所临道路类型、临近轨交站、高温、下雨、以及风力等级与共享单车停放需求显著相关;构建的预测模型总体预测准确率为77.5%,其中对出现频率最高的低停放需求预测准确率高达86.49%。     

Parking management for promoting sustainable transport in urban neighbourhoods. A review of existing policies and challenges from a German perspective

ABSTRACT

The organisation of parking is a key challenge to more sustainable mobility in urban areas, as its pricing and availability affect the rates of private car ownership and use. However, changing parking policies is a challenging issue for local politicians and planners because residents frequently oppose changes or restrictions to conditions they have taken for granted such as on-street parking in a public space. The aim of this paper is firstly to assess how the parking policy of an urban neighbourhood can be structured to contribute to more sustainable mobility and to increase liveability in the neighbourhood. The second aim is to apply the policies reviewed to an example neighbourhood. For this purpose, we systematically reviewed academic literature and identified five types of relevant parking policies: (i) maximum parking requirements, (ii) physical detachment of residence and parking space, (iii) residential parking permits and the limitation of available parking space, (iv) performance-based pricing and (v) parking as a demand management strategy. We discovered that most research focuses on econometric models about parking and that studies rarely address the effects of parking on the quality of life in neighbourhoods. Therefore, we need further research regarding the relationship of parking and liveability. We conclude that for the implementation of such parking policies in an example neighbourhood, the municipality needs to develop a mobility vision for its city. It has to understand parking as a tool for transportation demand management to increase the acceptance of parking policy concepts and to avoid spillover problems. Finally, in the German case, as in most other countries, states and municipalities need to redesign their legal frameworks to be able to manage parking supply better and to react to changes related to digital developments and parking. The findings have implications for other European neighbourhoods regarding the transfer from research to local circumstances and applications for the whole city.     

Stochastic Poisson game for an online decentralized and coordinated parking mechanism

This paper proposes a decentralized and coordinated online parking mechanism (DCPM), which seeks to reduce parking congestion at multiple parking facilities in a central business district (CBD) through guiding the parking decisions of a parking coordination group. To establish this DCPM, this study develops a stochastic Poisson game to model the competitions among parking vehicles en route at multiple parking facilities. The equilibrium condition for the proposed stochastic Poisson game is formulated through involving travelers’ parking choice behavior described by multinomial logit model. Furthermore, we prove that the stochastic Poisson game is a potential game with a unique equilibrium. A simultaneously updating distributed algorithm is developed to search the equilibrium solution of the DCPM. Its convergence is proved by both mathematical analysis and numerical experiments. The numerical experiments are conducted to test the efficiency of the DCPM, based on a real-world CBD covering Guicheng Community, Nanhai District at Foshan in China. The performance of the DCPM is compared to three greedy strategies following the nearest first, cheapest first, and least cruise first policies, respectively. The experimental results demonstrate that the DCPM significantly reduces cruise vehicles and average cruise distance per vehicle from all other three greedy strategies; the least cruise first strategy, which takes advantage of the real-time open spots information at parking facilities, performs better than the nearest first and the cheapest first strategies without the access to real-time information. The DCPM can further improve the benefit of the real-time information. Additionally, in terms of walking distance and parking cost, the DCPM provide a trade-off solution between the nearest first and the cheapest first strategies.     

System dynamics of urban traffic based on its parking-related-states

The urban parking and the urban traffic systems are essential components of the overall urban transportation structure. The short-term interactions between these two systems can be highly significant and influential to their individual performance. The urban parking system, for example, can affect the searching-for-parking traffic, influencing not only overall travel speeds in the network (traffic performance), but also total driven distance (environmental conditions). In turn, the traffic performance can also affect the time drivers spend searching for parking, and ultimately, parking usage. In this study, we propose a methodology to model macroscopically such interactions and evaluate their effects on urban congestion.The model is built on a matrix describing how, over time, vehicles in an urban area transition from one parking-related state to another. With this model it is possible to estimate, based on the traffic and parking demand as well as the parking supply, the amount of vehicles searching for parking, the amount of vehicles driving on the network but not searching for parking, and the amount of vehicles parked at any given time. More importantly, it is also possible to estimate the total (or average) time spent and distance driven within each of these states. Based on that, the model can be used to design and evaluate different parking policies, to improve (or optimize) the performance of both systems.A simple numerical example is provided to show possible applications of this type. Parking policies such as increasing parking supply or shortening the maximum parking duration allowed (i.e., time controls) are tested, and their effects on traffic are estimated. The preliminary results show that time control policies can alleviate the parking-caused traffic issues without the need for providing additional parking facilities. Results also show that parking policies that intend to reduce traffic delay may, at the same time, increase the driven distance and cause negative externalities. Hence, caution must be exercised and multiple traffic metrics should be evaluated before selecting these policies.Overall, this paper shows how the system dynamics of urban traffic, based on its parking-related-states, can be used to efficiently evaluate the urban traffic and parking systems macroscopically. The proposed model can be used to estimate both, how parking availability can affect traffic performance (e.g., average time searching for parking, number of cars searching for parking); and how different traffic conditions (e.g., travel speed, density in the system) can affect drivers ability to find parking. Moreover, the proposed model can be used to study multiple strategies or scenarios for traffic operations and control, transportation planning, land use planning, or parking management and operations.     

Electric vehicle parking in European and American context: Economic,energy and environmental analysis

The transportation sector faces increasing challenges related to energy consumption and local and global emissions profiles. Thus, alternative vehicle technologies and energy pathways are being considered in order to overturn this trend and electric mobility is considered one adequate possibility towards a more sustainable transportation sector.In this sense, this research work consisted on the development of a methodology to assess the economic feasibility of deploying EV charging stations (Park-EV) by quantifying the tradeoff between economic and energy/environmental impacts for EV parking spaces deployment. This methodology was applied to 4 different cities (Lisbon, Madrid, Minneapolis and Manhattan), by evaluating the influence of parking premium, infrastructure cost and occupancy rates on the investment Net Present Value (NPV). The main findings are that the maximization of the premium and the minimization of the equipment cost lead to higher NPV results. The NPV break-even for the cities considered is more “easily” reached for higher parking prices, namely in the case of Manhattan with the higher parking price profile. In terms of evaluating occupancy rates of the EV parking spaces, shifting from a low usage (LU) to a high usage (HU) scenario represented a reduction in the premium to obtain a NPV = 0 of approximately 14% for a 2500 € equipment cost, and, in the case of a zero equipment cost (e.g. financed by the city), a NPV = 0 was obtained with approximately a 2% reduction in the parking premium. Moreover, due to the use of electric mobility instead of the average conventional technologies, Well-to-Wheel (WTW) gains for Lisbon, Madrid, Minneapolis and Manhattan were estimated in 58%, 53%, 52% and 75% for energy consumption and 66%, 75%, 62% and 86% for CO₂ emissions, respectively.This research confirms that the success of deploying an EV charging stations infrastructure will be highly dependent on the price the user will have to pay, on the cost of the infrastructure deployed and on the adhesion of the EV users to this kind of infrastructure. These variables are not independent and, consequently, the coordination of public policies and private interest must be promoted in order to reach an optimal solution that does not result in prohibitive costs for the users.     

On the morning commute problem with bottleneck congestion and parking space constraints

Morning commuters choose their departure times based on a combination of factors—the chances of running into bottleneck congestion, the likely schedule delays, and parking space availability. This study investigates the morning commute problem with both bottleneck congestion and parking space constraints. In particular, it considers the situation when some commuters have reserved parking spots while others have to compete for public ones on a first-come-first-served basis. Unlike the traditional pure bottleneck model, the rush-hour dynamic traffic pattern with a binding parking capacity constraint varies with the relative proportions of the two classes of commuters. It is found that an appropriate combination of reserved and unreserved parking spots can temporally relieve traffic congestion at the bottleneck and hence reduce the total system cost, because commuters without a reserved parking spot are compelled to leave home earlier in order to secure a public parking spot. System performance is quantified in terms of the relative proportions of the two classes of commuters and is compared with those in the extreme cases when all auto commuters have to compete for parking and when none of them have to compete for one.     

Gender differences in travel behaviour and willingness to adopt sustainable behaviour

This paper presents pioneering research on gender differences in travel behaviour in southeastern Europe. The study analyses the socio-economic and main travel characteristics (particularly parking) of men and women. Additionally, it examines the influence of parking demand management measures on their behaviour, in terms of the willingness of men and women to reduce car use and to adopt more sustainable behaviour. The results reveal significant differences between genders in most of the characteristics examined. Moreover, it is shown that women are less car dependent and more sensitive to parking pricing and, therefore, more willing to replace a car journey with public transport. These findings suggest that gender should be an essential parameter when user behaviour is examined and modelled. Furthermore, the results indicate that gender differences must be considered when transport policy is created to affect the behaviour of men and women equally. The achievement of social equality is one of the primary objectives of sustainable development.     

Parking policy and urban goals: Linking strategy to needs

Parking management strategies have traditionally been used as a means of accommodating traffic demand, with little or no effort made to identify how such strategies might relate to other urban objectives. In this paper, parking management strategies are classified according to the control they exert over the amount of aggregate parking supply, access to parking, spatial distribution of parking supply, or dollar price of parking. Six general categories of urban policy objectives are identified, and the relationship between parking strategies and these objectives analyzed. The parking program in Baltimore is used to illustrate some of the relationships identified in this analysis. This paper concludes that the linkage between parking and the attainment of some urban objectives is potentially quite strong, and that further empirical research is needed to fully establish this relationship. Several different directions for further research are also identified.     

Maximum car ownership under constraints of road capacity and parking space

This paper presents a model for determining the maximum number of cars by zones in view of the capacity of the road network and the number of parking spaces available. In other words, the proposed model is to examine whether existing road network and parking supply is capable of accommodating future zonal car ownership growth (or the reserve capacity in each zone); i.e. the potential maximum zonal car ownership growth that generates the road traffic within the network capacity and parking space constraints. In the proposed model, the vehicular trip production and attraction are dependent on the car ownership, available parking spaces and the accessibility measures by traffic zones. The model is formulated as a bi-level programming problem. The lower-level problem is an equilibrium trip distribution/assignment problem, while the upper-level problem is to maximize the sum of zonal car ownership by considering travellers’ route and destination choice behaviour and satisfying the network capacity and parking space constraints. A sensitivity analysis based heuristic algorithm is developed to solve the proposed bi-level car ownership problem and is illustrated with a numerical example.     

Economic analysis of collecting parking fees by a private firm

The purpose of this paper is to show the possibility of a co-existence of public and private parking management systems even when all the parking spaces are owned by the government. This study focuses on the issue of collecting parking fees by a private firm that has been used by some local governments in Taiwan. We assume that the government behaves as a leader and a private firm as a follower in a Stackelberg three-stage game. At stage 1, the government selects its parking space. At stage 2, the government and the firm set their parking fees simultaneously. At the final stage, consumers (drivers) choose the parking lot between the space of the government and that of the firm by considering the full costs, consisting of the parking fee and the searching (with congestion) time cost. The objective of the government is to maximize welfare and that of the firm is to maximize profit. The model is constructed at first and a simulation analysis is then made. The result supports the strategy of adopting the franchise of collecting parking fees if the private firm is more efficient than the government. Moreover, the government may keep fewer parking spaces and release more parking spaces to the firm under the goal of maximizing welfare.     

Demand management in suburban settings

This paper provides findings from a review of employer based demand management strategies for reducing traffic congestion in several areas of the United States. The research was carried out by K.T. Analytics, Inc. in 1989 under a contract with the Federal Department of Transportation, Transportation Systems Center. Relying on a review of employer based programs in 26 sites, as well as selected literature, the paper draws conclusions about the effectiveness of employer based transportation systems management (programs to encourage carpooling, transit, biking, cycling, walking and flextime) and parking management (preferential parking for carpoolers, removal of parking subsidies, and other measures) aimed at reducing solo driving and peak period commuting.Several conclusions are offered for local planners and policy makers. Demand Management programs can be effective in reducing traffic congestion, provided they are targeted to larger employment centers, accompanied by transit development, high occupancy vehicle incentives and parking management strategies, especially pricing. Flextime should not be encouraged without careful evaluation as it may discourage ridesharing. Guaranteed ride home services appear promising and deserve attention. With all strategies, long term vigilance will be necessary as programs are susceptible to change over time. Stringent public policies do not appear necessary for development of effective programs. Ordinances should require plans not specific strategies, as it is difficult estimating the probable effectiveness of particular strategies. Ordinances should contain sanctions for employers not carrying out agreed to plans, and should include fees and financing to support public sector review of plans and on-going monitoring.     

Modelling parking choices considering user heterogeneity

We examine car driver’s behaviour when choosing a parking place; the alternatives available are free on-street parking, paid on-street parking and parking in an underground multi-storey car park. A mixed logit model, allowing for correlation between random taste parameters and estimated using stated choice data, is used to infer values of time, both when looking for a parking space and for accessing the final destination. Apart from the cost of parking, we found that vehicle age was a key variable when choosing where to park in Spain. We also found that the perception of the parking charge was fairly heterogeneous, depending both on the drivers’ income levels and whether or not they were local residents. Our results can be generalised for personalised policy making related with parking demand management.     

Determining intermediate origin-destination matrices for the analysis of composite mode trips

We consider in this paper the problem of determining intermediate origin-destination matrices for composite mode trips that involve a trip by private car to a parking facility and the continuation of the trip to the destination either by walking or by a transit mode. The intermediate origin-destination matrices relate to each component of the composite mode trip: a matrix from the trip origins to intermediate destinations which are parking lots and a matrix from the parking lots to the final destinations. The approach that we propose to solve this problem is to modify the entropy based trip distribution models to consider inequality constraints related to parking lot capacities. Such models may be easily calibrated by using well known calibration methods or generalization of these methods and may be easily solved by applying a primal feasible direction method of nonlinear programming.     

Solving the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP): A model to explore the impacts of self-driving vehicles on urban mobility

Interest in vehicle automation has been growing in recent years, especially with the very visible Google car project. Although full automation is not yet a reality there has been significant research on the impacts of self-driving vehicles on traffic flows, mainly on interurban roads. However, little attention has been given to what could happen to urban mobility when all vehicles are automated. In this paper we propose a new method to study how replacing privately owned conventional vehicles with automated ones affects traffic delays and parking demand in a city. The model solves what we designate as the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP), which dynamically assigns family trips in their automated vehicles in an urban road network from a user equilibrium perspective where, in equilibrium, households with similar trips should have similar transport costs. Automation allows a vehicle to travel without passengers to satisfy multiple household trips and, if needed, to park itself in any of the network nodes to benefit from lower parking charges. Nonetheless, the empty trips can also represent added congestion in the network. The model was applied to a case study based on the city of Delft, the Netherlands. Several experiments were done, comparing scenarios where parking policies and value of travel time (VTT) are changed. The model shows good equilibrium convergence with a small difference between the general costs of traveling for similar families. We were able to conclude that vehicle automation reduces generalized transport costs, satisfies more trips by car and is associated with increased traffic congestion because empty vehicles have to be relocated. It is possible for a city to charge for all street parking and create free central parking lots that will keep total transport costs the same, or reduce them. However, this will add to congestion as traffic competes to access those central nodes. In a scenario where a lower VTT is experienced by the travelers, because of the added comfort of vehicle automation, the car mode share increases. Nevertheless this may help to reduce traffic congestion because some vehicles will reroute to satisfy trips which previously were not cost efficient to be done by car. Placing the free parking in the outskirts is less attractive due to the extra kilometers but with a lower VTT the same private vehicle demand would be attended with the advantage of freeing space in the city center.     

The optimal dual-pricing policy of mall parking service

In studies of parking policy, the role of parking pricing has been addressed. Most researches have focused on the determination of a proper price for city parking spaces that are open to the public and it is now evident that price is used by authorities as a tool to manage transport demand. However, studies of parking pricing that pertain to privately-owned parking resources are few and in particular, the problem of setting a proper price for physical market parking has rarely been studied, such as a mall’s ‘dual-pricing portfolio’ decision for the simultaneous determination of a parking fee and the consumer spending required for free parking (i.e., the ‘threshold’). This is a common problem for most malls, but the different agents involved (e.g., the visitors, the mall, the marketplace and the parking lot departments) usually have diverse goals, so the decision must take account of a multiplicity of criteria and subtle relationships. In order to systematically support this type of inter-departmental decision process, a decision model that includes an analytical decision-aid process and the relevant programming models is established. A numerical example verifies the proposed model by taking the data for a mall in Taiwan and the implications, in terms of management, are given. This systematic computational model can be generalized to any type of commercial market that requires a (new) parking pricing policy.     

Interplay between electricity and transport sectors – Integrating the Swiss car fleet and electricity system

Electric vehicles are seen as a future mobility option to respond to long term energy and environmental problems. The 2050 Swiss energy strategy envisages 30–75% introduction of electric cars by 2050, which is designed to support the goal of decarbonising the energy sector. While the Swiss government has decided to phase out nuclear electricity, deployment of electric cars can affect electricity supply and emission trajectories. Therefore, potential interactions between the electricity and transport sectors must be considered in assessing the future role of electric mobility. We analyse a set of scenarios using the Swiss TIMES energy system model with high temporal resolution. We generate insights into cross-sectoral trade-offs between electricity supply and electrification/decarbonisation of car fleets. E-mobility supports decarbonisation of car fleet even if electricity is supplied from large gas power plants or relatively low cost sources of imported electricity. However, domestic renewable based electricity generation is expected to be too limited to support e-mobility. Stringent abatement targets without centralised gas power plants render e-mobility less attractive, with natural gas hybrids becoming cost effective. Thus the cost effectiveness of electric mobility depends on policy decisions in the electricity sector. The substitution of fossil fuels with electricity in transport has the potential to reduce revenues from fuel taxation. Therefore it is necessary to ensure consistency between electricity sector and transport energy policies.     

A novel permit scheme for managing parking competition and bottleneck congestion

Morning commuters may have to depart from home earlier to secure a parking space when parking supply in the city center is insufficient. Recent studies show that parking reservations can reduce highway congestion and deadweight loss of parking competition simultaneously. This study develops a novel tradable parking permit scheme to realize or implement parking reservations when commuters are either homogeneous or heterogeneous in their values of time. It is found that an expirable parking permit scheme with an infinite number of steps, i.e., the ideal-scheme, is superior to a time-varying pricing scheme in the sense that designing a permit scheme does not require commuters’ value of time information and the performance of the scheme is robust to the variation of commuters’ value of time. Although it is impractical to implement the ideal-scheme with an infinite number of steps, the efficiency loss of a permit scheme with finite steps can be bounded in both cases of homogeneous and heterogeneous commuters. Moreover, considering the permit scheme may lead to an undesirable benefit distribution among commuters, we propose an equal cost-reduction distribution of parking permits where auto commuters with higher value of time will receive fewer permits.     

Competitive strategies for original equipment manufacturers considering carbon cap and trade

This paper considers an original equipment manufacturer (OEM) faces competition from an independent remanufacturer (IR) and they both are regulated by carbon cap and trade policy (CTP). We develop models to explore the OEM’s optimal competitive strategy in the face of IR’s competition and environmental regulation. We first investigate the impact of CTP on the OEM and IR. Then, we analyze three competitive strategies that the OEM may choose: remanufacturing, fixed-fee licensing, and royalty licensing. We investigate their optimal decisions under each strategy and identify the conditions under which these strategies can coordinate the OEM and IR. Finally, we explore conditions under which one strategy is superior to another. The results show that the OEM is worse off when competing with the IR under CTP if the carbon cap allocated to the OEM is small. Fixed-fee licensing and royalty licensing can coordinate the OEM and IR not only from an economic perspective but also from an environmental perspective. The OEM’s optimal competitive strategy is determined by thresholds of three critical parameters: the fixed cost of setting up a remanufacturing system, the fixed-fee, and the per-unit royalty. We provide specific guidance on strategy selection for the OEM.