Mahmoud Mesbah

New Methodology for Optimizing Transit Priority at the Network Level

A new methodology for optimizing transit road space priority at the network level is proposed. Transit vehicles carry large numbers of passengers within congested road space efficiently. This aids justification of transit priority. Almost all studies that have investigated transit priority lanes focus at a link or an arterial road level, and no study has investigated road space allocation for priority from a network perspective. The aim of the proposed approach is to find the optimum combination of exclusive lanes in an existing operational transport network. Mode share is assumed variable, and an assignment is performed for both private and transit traffic. The problem is formulated by using bilevel programming, which minimizes the total travel time. The approach is applied to an example network and the results are discussed. The approach can identify the optimal combination of transit priority lanes and achieve the global optimum of the objective function. Areas for further development are discussed.

Use of smart card fare data to estimate public transport origin-destination matrix

Over the past few years, several techniques have been developed for using smart card fare data to estimate origin–destination (O-D) matrices for public transport. In the past, different walking distance and allowable transfer time assumptions had been applied because of a lack of information about the alighting stop for a trip. Such assumptions can significantly affect the accuracy of the estimated O-D matrices. Little evidence demonstrates the accuracy of O-D pairs estimated with smart card fare data. Unique smart card fare data from Brisbane, Queensland, Australia, offered an opportunity to assess previous methods and their assumptions. South East Queensland data were used to study the effects of different assumptions on estimated O-D matrices and to conduct a sensitivity analysis for different parameters. In addition, an algorithm was proposed for generating an O-D matrix from individual user transactions (trip legs). About 85% of the transfer time was non-walking time (wait and short activity time). More than 90% of passengers walked less than 10 min to transfer between alighting and the next boarding stop; this time represented about 10% of the allowable transfer time. A change in the assumed allowable transfer time from 15 to 90 min had a minor effect on the estimated O-D matrices. Most passengers returned to within 800 m of their first origin on the same day.

Policy-making tool for optimization of transit priority lanes in urban network

Transit improvement is an effective way to relieve traffic congestion and decrease greenhouse gas emissions. Improvement can be in the form of new facilities or giving on-road priority to transit. Although construction of off-road mass transit is not always viable, giving priority to transit can be a low-cost alternative. A framework is introduced for optimization of bus priority at the network level. The framework identifies links on which a bus lane should be located. Allocation of a lane to transit vehicles would increase the utility of transit, although this can be a disadvantage to auto traffic. The approach balances the impact on all stakeholders. Automobile advocates would like to increase traffic road space, and the total travel time of users and total emissions of the network could be reduced by a stronger priority scheme. A bilevel optimization is applied that encompasses an objective function at the upper level and a mode choice, a traffic assignment, and a transit assignment model at the lower level. The proposed optimization helps transport authorities to quantify the outcomes of various strategies of transit priority. A detailed sensitivity analysis is carried out on the relative weight of each factor in the objective function. The proposed framework can also be applied in the context of high-occupancy-vehicle lanes and heavy-vehicle priority lanes.

Bilevel Optimization Approach to Design of Network of Bike Lanes

A bike lane is an effective way to improve cycling safety and to decrease greenhouse gas emissions with the promotion of cycling. Improvements include high-quality off-road facilities and on-road bike lanes. Whereas construction of off-road lanes is not always possible because of urban land constraints and construction costs, on-road lanes can be a cost-effective alternative. An optimization framework for the design of a network of bike lanes in an urban road network was proposed. This framework identified links on which a bike lane could be introduced. Allocation of a lane to cyclists would increase the use of cycling, although it could disadvantage auto traffic. The presented approach balances the effects of a bike lane for all stakeholders. A bilevel optimization was proposed to encompass the benefits of cyclists and car users at the upper level and a model for traffic and bike demand assignment at the lower level. The objective function was defined by a weighted sum of a measure for private car users (total travel time) versus a measure for bike users (total travel distance on bike lanes). A genetic algorithm was developed to solve the bilevel formulation, which included introduction of a special crossover technique and a mutation technique. The proposed optimization will help transport authorities at the planning stage to quantify the outcomes of various strategies for active transport.

Development of a post-flood road maintenance strategy: case study Queensland, Australia

Abstract Currently, no road authority takes into account flooding in road deterioration (RD) models; as a result, post-flood rehabilitation treatments may be sub-optimal. This paper proposes a new approach to the development of a post-flood maintenance strategy. The recently developed roughness and rutting-based RD models with flooding, by the current authors, are used as input to predict pavement deterioration after a flood (i.e. assuming a flood in year 1). The HDM-4 model has been used to get the post-flood maintenance strategy with constrained and unconstrained budget, where post-flood rehabilitation starts from year 2. The road groups in state road network of Queensland, Australia, are used as the case study. The unconstrained budget solution aims to keep the network in an excellent condition at a cost of

Visualization of Geographical Information System and Automatic Vehicle Location Data to Explore Transit Performance

49.7bn with the possible strongest treatments. The constrained budget strategy uses agency cost and pavement performance as constraints in optimisation and provides a reasonable solution. This strategy requires about

Using delay functions to evaluate transit priority at signals

26.1bn in life cycle, which is close to the main road authority of Queensland’s post-flood rehabilitation programme. The paper discusses two other strategies on maximise economic benefits and budget optimisation. It is expected that a road authority would properly investigate its flood-damaged roads before implementation. The paper shows pavement performances with the post-flood strategy. The need for a RD model to predict deterioration after a flood and for post-flood treatment selection is also highlighted.

Evaluation of effects from sample-size origin-destination estimation using smart card fare data

This paper describes a new methodology to explore the operational performance of on-road transit on a network-level basis. The approach mines stop-level automatic vehicle location (AVL) data and uses new visualization methodologies in geographical information systems (GIS) to explore spatial and temporal patterns of changes in operational performance. The focus is a large historical AVL data set for the tram (streetcar) network of Melbourne, Australia. A major aim is to illustrate how a spatial perspective on networks can improve understanding of transit operational performance. AVL records for 24,451 tram vehicle trips in March 2001 and 26,155 trips in March 2004 were analyzed for 175 tram stops. Arrival travel times and differences between scheduled and actual arrival times were computed for each stop. The Arcview GIS system and spatial surface interpretation with the inverse distance weighted methodology were used to explore spatial variations in the data. Five analyses explored network variations in operational performance: (a) an analysis of tram travel time to Melbournes central business district, (b) a reliability analysis exploring variations between actual versus scheduled travel time for 2001, (c) an examination of 2001 tram travel time variation with the use of the coefficient of variation, (d) an analysis of changes in tram travel times between 2001 and 2004, and (e) a trend analysis of changes in tram reliability from 2001 to 2004 with the use of the coefficient of variation. Overall the analysis demonstrated that a networkwide and spatial perspective in exploring the operational performance of large transit systems was a new, original, and worthwhile approach to identify transit issues for further investigation.

Estimating Pavement's Flood Resilience

In this paper a new method to evaluate the network-wide effect of transit signal priority (TSP) is presented. A delay function is developed to estimate the effect of TSP at intersections using parameters such as traffic flow and signal characteristics. The proposed method is tested in two case studies. Firstly, it is applied to a single intersection to address both TSP strategy and model efficiency. Secondly, a set of TSP implementation scenarios is defined for a corridor in South-East Queensland, Australia. Bus and car delays are calculated for each scenario and the results are compared with those obtained using microsimulation. The results confirm that the delay estimated by the proposed method closely matches microsimulation results. The proposed method reflects the effects of having TSP at each intersection without performing time consuming microsimulation analyses. This method makes it possible to evaluate the effectiveness of TSP in large networks. The new method can be used to perform preliminary evaluation of TSP schemes at the network level, thereby potentially reducing the significant computational time of network analyses.

Trip Detection with Smartphone-Assisted Collection of Travel Data

Public transport planners are required to make decisions on transport infrastructure and services worth billions of dollars. The decision-making process for transport planning needs to be informed, accountable, and founded on comprehensive, current, and reliable data. One of the major issues affecting the accuracy of the estimated origin-destination (O-D) matrices is sample size. Cost, time, precision, and biases are some issues associated with sample size. Smart card data can potentially provide much information based on better understanding and assessment of the sample size impact on the estimated O-D matrices. This paper uses South East Queensland (SEQ) data to study the effect of different data sample sizes on the accuracy level of the generated public transport O-D matrices and to quantify the sample size required for a certain level of accuracy. As a result, the total number of O-D trips for the whole network can be accurately estimated at all levels of sample sizes. However, a wide distribution of O-D trips appeared at different sample sizes. The large difference from the actual distribution at 100% sample size was readily captured at small sample sizes where more O-D pairs were not representative. The wide distribution of O-D trips at different levels of sample sizes caused significant errors even at large sample sizes. The variation of the errors within the same sample was also captured as a result of the 80 iterations for each sample size. It is concluded that three major parameters (distribution, number, and sample size of selected stations) have a significant impact on the estimated O-D matrices. These results can be also reflected on the sample size of the traditional O-D estimation methods, such household travel surveys.