A study of passenger discomfort measures at the Hong Kong mass transit railway system

This note investigates the important attributes relating to the crowding effects at the Mass Transit Railway (MTR) stations in Hong Kong. Data was collected at two sets of three MTR stations to study the responses of the passengers due to the discomfort at crowded vehicles and platforms. Stated preference surveys were used to study the effects of passenger discomfort measures.     

Short‐term travel time forecasts for transport information system in hong kong

In this paper, a case study is carried out in Hong Kong for demonstration of the Transport Information System (TIS) prototype. A traffic flow simulator (TFS) is presented to forecast the short‐term travel times that can be served as a predicted travel time database for the TIS in Hong Kong. In the TFS, a stochastic deviation coefficient is incorporated to simulate the minute‐by‐minute fluctuation of traffic flows within the peak hour period. The purposes of the case study are: 1) to show the applicability of the TFS for larger‐scale road network; and 2) to illustrate the short‐term forecasting of path travel times in practice. The results of the case study show that the TFS can be applied to real network effectively. The predicted travel times are compared with the observed travel times on the selected paths for an OD pair. The results show that the observed path travel times fall in the 90% confidence interval of the predicted path travel times.     

A review of the road safety strategy in hong kong

Since the mid-1990s, the effectiveness of road safety measures in Hong Kong has been weakening. Six administrations in Australia, California, Great Britain (GB), Japan, New Zealand and Sweden are selected to help review the road safety activities in Hong Kong. Nine main components of the road safety strategy, including vision, objectives, targets, action plan, evaluation and monitoring, research and development, quantitative modeling, institutional framework and funding are summarized from the road safety strategies of these overseas administrations and compared to that of Hong Kong. It is found that Hong Kong's road safety activities have to be restructured to make significant improvement. In the future, a new approach structured by the nine different road safety components is recommended. The lessons learnt can be generalized to smooth the progress of other administrations at the Intermediate Stage towards the Advanced Stage of road safety development by using the short-, medium- and long-term approaches.     

Estimation of train dwell time at short stops based on track occupation event data: A study at a Dutch railway station

Train dwell time is one of the most unpredictable components of railway operations, mainly because of the varying volumes of alighting and boarding passengers. However, for reliable estimations of train running times and route conflicts on main lines, it is necessary to obtain accurate estimations of dwell times at the intermediate stops on the main line, the so‐called short stops. This is a great challenge for a more reliable, efficient and robust train operation. Previous research has shown that the dwell time is highly dependent on the number of boarding and alighting passengers. However, these numbers are usually not available in real time. This paper discusses the possibility of a dwell time estimation model at short stops without passenger demand information by means of a statistical analysis of track occupation data from the Netherlands. The analysis showed that the dwell times are best estimated for peak and off‐peak hours separately. The peak‐hour dwell times are estimated using a linear regression model of train length, dwell times at previous stops and dwell times of the preceding trains. The off‐peak‐hour dwell times are estimated using a non‐parametric regression model, in particular, the k‐nearest neighbor model. There are two major advantages of the proposed estimation models. First, the models do not need passenger flow data, which is usually impossible to obtain in real time in practice. Second, detailed parameters of rolling stock configuration and platform layout are not required, which makes the model more generic and eases implementation. A case study at Dutch railway stations shows that the estimation accuracy is 85.8%–88.5% during peak hours and 80.1% during off‐peak hours, which is relatively high. We conclude that the estimation of dwell times at short stop stations without passenger data is possible. Copyright © 2016 John Wiley & Sons, Ltd.     

In-train air quality assessment of the railway transit system in Beijing: A note

This study examines the concentrations of air pollutants in passenger carriages on a number of lines of the Beijing railway transit system differentiating between services with and without air conditioning. In-train air quality monitoring found PM₁₀ concentrations are extremely high compared with other cities. Integrated factor assessment results show that the in-train air quality in the ground railway transit system where there is air conditioning is more acceptable than in the underground system.     

The effect of railway privatization on train planning: A case study of the UK

The restructuring of the UK railway industry in preparation for privatization led to major changes being made to train planning processes. Subsequent train planning problems, some of which became very public, suggest that something went seriously wrong during the development or implementation of these revised processes. This paper investigates what went wrong and why, finding that several factors were involved, including the objectives the new processes were expected to meet and the software that was being developed to support the new processes. There are clear lessons to be learnt from the UK experience to inform debate on future railway restructuring initiatives.     

A new approach for improving the performance of freight train timetabling of a single-track railway system

A new approach for improving the performance of freight train timetabling for single-track railways is proposed. Using the idea of a fixed-block signaling system, we develop a matrix representation to express the occupation of inter- and intra-station tracks by trains illustrating the train blocking time diagram in its entirety. Train departure times, dwell times, and unnecessary stopping are adjusted to reduce average train travel time and single train travel time. Conflicts between successive stations and within stations are identified and solved. A fuzzy logic system is further used to adjust the range of train departure times and checks are made to determine whether dwell times and time intervals can be adjusted for passenger and freight trains at congested stations to minimize train waiting times. By combining manual scheduling expertise with the fuzzy inference method, timetable efficiency is significantly improved and becomes more flexible.     

Estimation of AADT from short period counts in hong kong — A comparison between neural network method and regression analysis

The average annual daily traffic (AADT) volumes can be estimated by using a short period count of less than twenty‐four hour duration. In this paper, the neural network method is adopted for the estimation of AADT from short period counts and for the determination of the most appropriate length of counts. A case study is carried out by analysing data at thirteen locations on trunk roads and primary roads in urban area of Hong Kong. The estimation accuracy is also compared with the one obtained by regression analysis approach. The results show that the neural network approach consistently performed better than the regression analysis approach.     

A simulation model of train travel on a rail transit line

A simplified simulation model for the operational analysis of a rail rapid transit train is presented. The model simulates the movement of a train along a route, and develops the relationships of time—distance, time—speed and distance—speed. The inputs to the model are the profile of speed limits and the dynamic characteristics of the train. Without the information on the track geometry and tractive effort, the model determines the speed of the train at a location based on the previous and future speed limits relative to the location. It was found that the model can fairly accurately simulate the relationship between travel time and distance. A comparison of the train travel times between the actual and simulated runs is presented. Because of the simplicity of input and calculation method, the model can be a useful tool for the “desk-top” analysis of frequently occurring planning problems of a commuter rail or rail rapid transit line, such as the impacts of changes in speed limits, station locations, station stopping policy, addition/elimination of stations, and types of rail cars.     

Comparative analysis of transit assignment: evidence from urban railway system in the Tokyo Metropolitan Area

This paper empirically compares the performance of six traffic assignment methods using the same empirical dataset of route choice. Multinomial logit (MNL), structured multinomial probit (SMNP), user equilibrium (UE), logit-based stochastic user equilibrium (SUE), probit-based SUE, and all-or-nothing (AON) assignment methods are applied to the comparative analysis. The investigated methods include those with three types of error components in their cost functions and two types of flow dependencies. Four methods of generating the route choice set are also compared for use as stochastic traffic assignment methods. The revealed preference data of urban rail route choice in the Tokyo Metropolitan Area are used for the case analysis. The empirical case analysis shows that probit-based SUE provides the best accuracy but requires the longest computation time. It also shows that the heuristics used to generate the choice set influence the method’s accuracy, while the incorporation of route commonality and in-vehicle congestion significantly improves its accuracy. Finally, the implications for practical rail planning are discussed on the basis of the analysis results.     

Management objectives and the causes of mass transit deficits

Based on data for the period from 1948 to 1997, exogenous decreases in demand and increases in costs are estimated to have reduced the annual profitability of the Chicago Transit Authority (CTA) by $1 billion. Half of this decline was recouped by reductions in service, increased fares and increased productivity. Even more would have been recouped had the CTA not given away earlier productivity gains during the 1970s. This was during a period when subsidies were increasing rapidly, and seemingly without constraint. When faced with financial challenges, management has preferred to increase fares rather than reduce service levels to the detriment of social welfare.     

A new look at the rate of change of energy consumption with respect to journey time on an optimal train journey

We present a new derivation of a key formula for the rate of change of energy consumption with respect to journey time on an optimal train journey. We use a standard mathematical model (Albrecht et al., 2015b; Howlett, 2000; Howlett et al., 2009; Khmelnitsky, 2000; Liu and Golovitcher, 2003) to define the problem and show by explicit calculation of switching points that the formula also applies for all basic control subsequences within the optimal strategy on appropriately chosen fixed track segments. The rate of change was initially derived as a known strictly decreasing function of the optimal driving speed in a text edited by  Isayev (1987, Section 14.2, pp 259–260) using an empirical resistance function. An elegant derivation by Liu and Golovitcher (2003, Section 3) with a general resistance function required an underlying assumption that the optimal strategy is unique and that the associated optimal driving speed is a strictly decreasing and continuous function of journey time. An earlier proof of uniqueness (Khmelnitsky, 2000) showed that the optimal driving speed decreases when journey time increases. A subsequent constructive proof (Albrecht et al., 2013a, 2015c) used a local energy minimization principle to find optimal switching points and show explicitly that the optimal driving speed is a strictly decreasing and continuous function of journey time. Our new derivation of the key formula also uses the local energy minimization principle and depends on the following observations. If no speed limits are imposed the optimal strategy consists of a finite sequence of phases with only five permissible control modes. By considering all basic control subsequences and subdividing the track into suitably chosen fixed segments we show that the key formula is valid on each individual segment. The formula is extended to the entire journey by summation. The veracity of the formula is demonstrated with an elementary but realistic example.     

An investigation of passenger interchange and train standing time at LRT stations: (ii) Estimation of standing time

The standing-time of trains at urban rail stations is pertinent to determining the line capacity and fleet size. The assumption of uniform boarding and alighting leads to under-estimation of the standing time. It is shown that the train standing-time is related to the fraction of boarders and the maximum demand for boarding and alighting at a door. It is further shown that the probability distribution of passengers at a door depends on the platform entrance locations. A methodology that takes into account the above factors is proposed for estimating the train standing-time.     

A stochastic optimization model for transit network timetable design to mitigate the randomness of traveling time by adding slack time

Transit network timetabling aims at determining the departure time of each trip of all lines in order to facilitate passengers transferring either to or from a bus. In this paper, we consider a bus timetabling problem with stochastic travel times (BTP-STT). Slack time is added into timetable to mitigate the randomness in bus travel times. We then develop a stochastic integer programming model for the BTP-STT to minimize the total waiting time cost for three types of passengers (i.e., transferring passengers, boarding passengers and through passengers). The mathematical properties of the model are characterized. Due to its computational complexity, a genetic algorithm with local search (GALS) is designed to solve our proposed model (OPM). The numerical results based on a small bus network show that the timetable obtained from OPM reduces the total waiting time cost by an average of 9.5%, when it is tested in different scenarios. OPM is relatively effective if the ratio of the number of through passengers to the number of transferring passengers is not larger than a threshold (e.g., 10 in our case). In addition, we test different scale instances randomly generated in a practical setting to further verify the effectiveness of OPM and GALS. We also find that adding slack time into timetable greatly benefits transferring passengers by reducing the rate of transferring failure.     

Estimation of the perceived value of transit time for containerized cargoes

This paper proposes a novel method for estimating the perceived value of transit time of containers by shipping lines. The key idea is that a shipping line’s published schedule is the optimal decision that minimizes the sum of fuel cost and time-associated costs of the containers adopted by the shipping line. Using the proposed method, we find that the adopted values of transit time for nine trans-Pacific services operated by Orient Overseas Container Line and five trans-Pacific services operated by Maersk Line are between US$5/TEU/day and US$30/TEU/day. We further demonstrate how the adopted value can be used for designing the optimal transit times between ports, analyzing the viability of slow-steaming, checking whether ships should speed up to catch up to connecting ships on other services, and helping to predict the market share of less polluting fuels in view of rules on air emission.     

The importance of attitudes in the decision to use mass transit

This paper reports the results of tests of the hypotheses that attitudinal variables are important in mode choice decisions and that they can significantly increase the explanatory power of network-based mode choice models. Conflicts between the results of previous work by Lovelock and Johnson are resolved by this study. Attitudinal items used by Johnson and by Lovelock in separate studies in the San Francisco Bay area were included in a survey of Chapel Hill households. Tests of the incremental explanatory power of the attitudinal variables in mode choice models confirm that the items used by Johnson do not contribute to the explanatory power of models using network time and cost data. Similar tests showed that Lovelock's attitudinal items do significantly increase the predictive ability of the models. The conflicting results of these previous studies are therefore due to the content of the items. Attitudinal data, including both attitude items and measures of perceptions of system attributes, do enhance the predictive power of models involving network data.This research was supported by a grant from the Urban Mass Transportation Administration, U.S. Department of Transportation, Washington, D.C.     

Evaluating security of rail transit systems: A metric system approach

Crime and fear of crime is a major problem plaguing U.S. transit systems, particularly those serving large urban areas. This paper presents a normative framework for assessing rail transit security following a system-wide metric approach. The security metric can also be used to assess the marginal improvement in security as a result of improving or adopting alternative policing and monitoring strategies. The model consists of five tasks: surveying rail transit security systems, developing a rail transit security metric, assigning efficiency ratings to rail security functions, developing a composite index for the efficiency of the overall security system, and applying a probability matrix to temper the results. Efficiency ratings can be translated into probability of occurrence figures that can be used in a decision tree context to improve rail transit security.     

A time series analysis of monthly ridership for an urban rail rapid transit line

This paper presents two time series regression models, one in linear form and the other in logarithmic form, to estimate the monthly ridership of a single urban rail rapid transit line. The model was calibrated for a time period of about six and a half years (from 1978–1984) based on ridership data provided by a transit authority, gasoline prices provided by a state energy department, and other data.The major findings from these models are: (1) seasonal variations of ridership are –6.26%, or –6.20% for the summer period, and 4.77%, or 4.62% for the October period; (2) ridership loss due to a station closure is 2.46% or 2.41%; and (3) elasticities of monthly ridership are –0.233 or –0.245 with respect to real fare, 0.113 or 0.112 with respect to real gasoline price, and 0.167 or 0.185 with respect to real bridge tolls for the competing automobile trips. Such route specific application results of this inexpensive approach provide significant implications for policymaking of individual programs in pricing, train operation, budgeting, system changes, etc., as they are in the case reported herein and would be in many other cities.     

A web-based support system for estimating and visualizing the emissions of diesel transit buses

Diesel transit buses are heavy-duty vehicles that are major sources of greenhouse gases and toxic pollutants. Although various models have been used to estimate their emissions, it has been difficult to effectively apply these estimation models due to the need for user-friendly interfaces, the large amounts of underlying data, and the potential data inaccuracy. In this paper, we present a web-based support system developed for transit operators who need to estimate and visualize the emissions of diesel transit buses where a micro-scale Vehicle Specific Power approach is used to estimate emissions based on global positioning system data. Case studies show that the web-based support system provides a user-friendly environment that makes it easier to apply emission estimation methodologies and visualize emissions.