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Extreme weather conditions, strong gusts, and torrential rainfall threaten the safety of the general public and restrict people’s travel options. Most of the transportation modes are suspended because of safety reasons. Taxis are one of the only few available non-private transport modes to provide services to those who have urgent and unavoidable travel needs. This study uses global positioning system data collected from 460 Hong Kong urban taxis during nine ordinary and one tropical cyclone periods aiming to find out and explain the differences in relation to the percentage of taxis not in operation, the number of served passenger-trips, average time spent by vacant-taxi drivers finding a customer, and the percentage of taxi drivers in cross-district customer-search throughout the same 48 h duration. The findings show an inadequate level of taxi supply and a high passenger demand during the tropical-cyclone-affected period. Up to 80% of taxis were not in operation to serve the urgent and necessary trips. The average customer-search time for taxi drivers, which is anticipated inversely proportional to the demand for taxi rides, was very short (about 5 min). Policy measures are discussed and recommended to the government to improve the taxi services during extreme weather conditions.

ABSTRACTThis paper proposes a simultaneous route and departure time choice (SRDTC) problem with fixed demand in a degradable transport network. In this network, travelers face with stochastic travel times. Their selection of routes and departure times follows the UE principle in terms of the mean generalized route cost, which is defined as the probabilistic dynamic user optimal (PDUO) principle. The proposed PDUO-SRDTC problem is formulated as a variational inequality (VI) problem. As a special case of PDUO-SRDTC problem, the PDUO route choice (PDUO-RC) problem is also proposed and formulated as a VI problem. Network degradation is defined on the degradation of the outflow capacity of each link. A Monte Carlo-based stochastic link transmission model (MC-SLTM) is developed to capture the effect of physical queues and the random evolution of traffic states during flow propagation to estimate mean generalized route costs. Both the extragradient algorithm and the route-swapping method with a variable sample size scheme are developed to solve the proposed VI problems. Numerical examples are developed to illustrate the paradoxical phenomena of the problems and the effectiveness of the solution methods. Numerical results show that constructing a new road, enhancing link outflow capacity, or reducing outflow capacity degradation can lead to poor network performance and it is important to consider both network degradation and queue spillback when making transportation policies aimed at improving network performance. The results also demonstrate that the variable sample size scheme can give a quicker and better solution than the traditional fixed sample size scheme.

ABSTRACTThis paper proposes an intersection-movement-based variational inequality formulation for the multi-class dynamic traffic assignment (DTA) problem involving physical queues using the concept of approach proportion. An extragradient method that requires only pseudomonotonicity and Lipschitz continuity for convergence is developed to solve the problem. We also present a car–truck interaction paradox, which states that allowing trucks to travel or increasing the truck flow in a network can improve network performance for cars in terms of the total car travel time. Numerical examples are set up to illustrate the importance of considering multiple vehicle types and their interactions in a DTA model, the effects of various parameters on the occurrence of the paradox, and the performance of the solution algorithm.

ABSTRACTThis paper proposes a bi-level risk-averse network design model for transportation networks with heterogeneous link travel time distributions. The objective of the network design is to minimise the total system travel time (TSTT) budget (TSTTB), which consists of the mean TSTT and a safety margin. The design is achieved by selecting optimal link capacity expansions subject to a fixed expansion budget. Users’ selfish behaviour and risk attitude are captured in the lower level traffic assignment constraints, in which travellers select routes to minimise their own path travel time budget. The properties of the design problem are analysed analytically and numerically. The analysis shows that despite the lack of knowledge of travel time distributions, the probabilities that the actual TSTT and the actual path travel time are, respectively, within the optimal TSTTB and the minimum path travel time budget under optimal design have lower bounds. The lower bounds are related to the system manager's and travellers’ risk aversion. The optimal TSTTB is proven to be bounded below even when the link expansion budget is unlimited.

This paper presents a global optimisation framework for identifying the most critical combination of vulnerable links in a transportation network. The problem is formulated as a mixed-integer non-linear programme with equilibrium constraints, aiming to determine the combination of links whose deterioration would induce the most increase in total travel cost in the network. A global optimisation solution method applying a piecewise linearisation approach and range-reduction technique is developed to solve the model. From the numerical results, it is interesting and counterintuitive to note that the set of most vulnerable links when simultaneous multiple-link failure occurs is not simply the combination of the most vulnerable links with single-link failure, and the links in the critical combination of vulnerable links are not necessarily connected or even in the neighbourhood of each other. The numerical results also show that the ranking of vulnerable links will be significantly affected by certain input parameters.

Special events attract extraordinarily high travel demands over a relatively short time. Managing the travel demand for special events is more difficult than for normal commuting trips. Grave-sweeping is a special event that is popular in Asia, especially in Chinese societies. In Hong Kong, many people visit cemeteries and columbaria around the two traditional festivals, causing overcrowding at the sites and congestion on the access roads. To mitigate the adverse traffic effects, the police usually implement temporary traffic management measures to control the number of visitors accessing the sites. However, these measures often ignore visitors’ arrival time preferences and hence their effectiveness is questionable. This study models and analyses the arrival time preferences of cemetery and columbarium visitors under various transport mode scenarios. The model development is based on 3128 choice decisions collected from 782 respondents. A mixed logit model is calibrated to investigate the potential taste heterogeneity of the respondents, and a likelihood ratio test demonstrates that the model is superior to a standard multinomial logit model and provides a better fit to the survey data. The model results indicate that in-vehicle travel time, total waiting and walking time, and the provision and travel fare of feeder services are significant factors influencing cemetery and columbarium visitors’ arrival time choices. The effects of socio-economic factors are also examined and discussed. Based on the model results, recommendations are provided for managing the travel demand to cemeteries and columbaria at peak and off-peak hours on or around festival days.

A hybrid enhanced artificial bee colony algorithm (HEABC) is proposed for solving the problem of bus network design. The algorithm is intended to reduce the weighted sum of the number of transfers and the total travel time of the users through restructured bus routes and new frequencies without increased fleet sizes. The HEABC relies mainly on the enhanced artificial bee colony algorithm to determine the route structure, and the frequency is determined by the frequency-setting heuristic during the fitness evaluation. For an illustration of its performance, the HEABC was compared with a hybrid generic algorithm and a variant of the HEABC. The results indicated that the HEABC could produce better solutions than the other two algorithms could. Moreover, the HEABC could produce a design that was better than the existing design for maximum intermediate stops, total travel time, number of transfers, maximum headway, and total fuel cost. The design should be acceptable to the public and to bus operators.

Traditionally, game theoretic approaches to measuring transport network reliability have relied on the outcome of a game played between network users seeking to minimize their travel costs and an origin-destination-specific (O-D-specific) demon that seeks the opposite by damaging links in the network. This problem assumes the presence of only one demon in each O-D pair and assumes the capacity reduction to be 50% if the link is selected for damage by one or more O-D-specific demons. The game is typically expressed as a path-based formulation, which is computationally intensive since the formulation requires path enumeration. With relaxation of the assumptions on the O-D-specific nature of the demons and the capacity reduction, a link-based multiple network demon formulation is proposed by the nonlinear complementarity problem approach, in which each demon is free to select any link to damage. Within this framework, the effects of the proposed model on total expected network cost and reliability measures are examined, and specific examples demonstrate the paradoxical phenomenon that if one adds a road to a network, then all travelers may be worse off for total expected network cost or travel time reliability. Overall, the results indicate the importance of assumptions used to total expected network cost and reliability measures and provide some insights into the problem of ignoring these paradoxical phenomena in reliable and robust network design.

The lagged cell-transmission model (L-CTM) is an enhanced version of the CTM. Both can be incorporated into a dynamic traffic assignment framework for offline transport planning and policy evaluation and online intelligent transportation system applications. In contrast to the CTM, the L-CTM adopts a nonconcave flow-density relation, which can be used to predict the existence of rather dense traffic in queues coasting toward the end of the queue or to help disprove the existence of this phenomenon. However, this study shows that the L-CTM can yield unrealistic densities, namely, negative densities and densities higher than theoretical jam density, the former of which has not been addressed in the literature. To cope with these unrealistic results, this study improves the L-CTM by introducing one more term in each sending and receiving function of the model. The improved model, the enhanced L-CTM (EL-CTM), is proved to yield nonnegative densities not greater than the jam density but can still allow the use of nonconcave density relations. The EL-CTM yields Lighthill-Whitham-Richards solutions when cell lengths and time intervals tend to zero and includes the CTM and the L-CTM as special cases. The EL-CTM is also shown to give more accurate solutions than the L-CTM (and hence also the CTM) does under a small increase in computation time. Hence the EL-CTM is believed to be more suitable for both online and offline applications in the future.

Dynamic Traffic Assignment (DTA) is long recognized as a key component for network planning and transport policy evaluations as well as for real-time traffic operation and management. How traffic is encapsulated in a DTA model has important implications on the accuracy and fidelity of the model results. This study compares and contrasts the properties of DTA modelled with point queues versus those with physical queues, and discusses their implications. One important finding is that with the more accurate physical queue paradigm, under certain congested conditions, solutions for the commonly adopted dynamic user optimal (DUO) route choice principle just do not exist. To provide some initial thinking to accommodate this finding, this study introduces the tolerance-based DUO principle. This paper also discusses its solution existence and uniqueness, develops a solution heuristic, and demonstrates its properties through numerical examples. Finally, we conclude by presenting some prospective future research directions.

Planning road network improvements over a long horizon often faces uncertainty. Under resource constraints, the government needs to carefully select a design strategy so as to cope with uncertainty and achieve its objective(s). Through sensitivity analyses, this paper discusses the social and financial aspects of three government's network design strategies, namely government-as-the-provider (GP), monopoly market (MM), and competitive market (CM). The first is the government acts as the sole toll road operator who provides the infrastructure but collects just enough tolls to recover costs. The second is the private sector builds all the toll roads based on the build-operate-transfer scheme and collects tolls in a monopoly manner. The third is the government allows multiple operators to build different toll roads but collect tolls under the build-operate-transfer scheme in a competitive manner. The results show that even though the GP strategy would produce the best total discounted consumer surplus (TDCS), maintaining cost recovery might be a problem. On the other hand, the MM strategy would mostly likely be profitable to the operators, at the expense of producing a much lower TDCS. It appears that the CM strategy would maintain a good balance between TDCS and profitability under uncertainty. This finding is consistent with the current trend of privatizing government services to multiple companies.

As part of Advanced Transportation Information Systems (ATIS), route planning and guidance (RPG) services have been proposed for potential travel time savings and congestion reduction effects. This study extends previous studies by investigating their effect on consumer surplus and analyzing the tradeoff between network capacity expansions versus RPG services subsidy. Eventually, we analyze whether and to what extent can RPG services substitute network expansion. To answer these questions, this paper develops a mixed-equilibrium model for elastic travel demand and elastic market penetration of RPG services. Drivers are assumed to follow the stochastic user equilibrium conditions, with the drivers equipped with RPG services having lower travel time perception variations. This model is formulated as a nonlinear complementarity problem for exposition clarity and is solved as a variational inequality problem for efficiency, A numerical study is provided to demonstrate the approach.