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Bicycle is a sustainable low-carbon transport mode. However, insufficient or unplanned infrastructure leads to decrease in the share of bicycle in many cities of developing nations. In order to increase the bicycle share and to provide safer, faster and more direct routes, a bicycle superhighway is proposed for urban areas. This study identifies the potential of increase in the bicycle share. For maximum utilization of the new infrastructure, an algorithm is presented to identify the optimum number and locations of the connectors between proposed new infrastructure and existing network. Household income levels are incorporated into the decision making process of individual travellers for a better understanding of the modal shift. A real-world case study of Patna, India is chosen to show the application of the proposed superhighway. It is shown that for Patna, the bicycle share can escalate as high as 48% up from 32% by providing this kind of infrastructure. However, together with bicycles, allowing motorbikes on the superhighway limits the bicycle share to 44%. The increase in bicycle share is mainly a result of people switching from motorbike, public transport and walk to the bicycle. Further, to evaluate the benefits of the bicycle superhighway, this study first extends an emission modelling tool to estimate the time-dependent, vehicle-specific emissions under mixed traffic conditions. Allowing only bicyclists on the superhighway improves congested urban areas, reduces emissions, and increases accessibility. However, allowing motorbikes on the superhighway increases emissions significantly in the central part of the urban area and reduces accessibilities by bicycle mode to education facilities which are undesirable. This study elicits that a physically segregated high-quality bicycle superhighway will not only attract current noncyclist travellers and increase the share of the bicycle mode, but will also reduce negative transport externalities significantly.

Cycling as an inexpensive, healthy, and efficient mode of transport for everyday traveling is becoming increasingly popular. While many cities are promoting cycling, it is rarely included in transport models and systematic policy evaluation procedures. The purpose of this study is to extend the agent-based transport simulation framework MATSim to be able to model bicycle traffic more realistically. The network generation procedure is enriched to include attributes that are relevant for cyclists (e.g. road surfaces, slopes). Travel speed computations, plan scoring, and routing are enhanced to take into account these infrastructure attributes. The scoring, i.e. the evaluation of simulated daily travel plans, is

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An activity-based approach to transport demand modeling is considered the most behaviorally sound procedure to assess the impacts of transport policies. In this paper, it is investigated whether it is possible to transfer an estimated model for activity generation from elsewhere (the estimation context) and use local area (application context) traffic counts to develop a local area activity-based transport demand representation. Here, the estimation context is the Dallas-Fort Worth area, and the application context is Berlin, Germany. Results in this paper suggest that such a transfer approach is feasible, based on comparison with a Berlin travel survey. Additional studies in the future need to be undertaken to examine the stability of the results obtained in this paper.Keywords: Activity-based Demand Modeling, Agent-based Simulation, Transport Modeling, Model TransferabilityZiemke, Nagel, Bhat 3 INTRODUCTIONTraffic assignment models are useful tools to predict reactions of the transport system to policy measures. Traditional assignment models are static, taking constant OD flows as input, and producing static congestion patterns as output. In order to address dynamic policy measures such as a peak hour toll or changes of the opening times of workplaces and/or shops, dynamic traffic assignment (DTA) has emerged as a useful analysis approach (1). Originally, DTA typically took time-dependent (hourly or day period) OD matrices as input; more recent approaches (e.g. TRANSIMS (2) or DynusT (3)) often take as input lists of trips where each trip is defined by the triplet of departure time, departure location, and destination location. It is clear that one can go one step further and take full daily plans as input. To the authors' knowledge, MATSim (Multi-Agent Transport Simulation (4)) is the only model system doing this at the large (regional) scale. The advantages of using complete daily activity-travel plans as DTA inputs include that all kinds of precedence constraints, such as the fact that a person cannot leave an activity location before having arrived, are automatically resolved. Also, such a model can accommodate more behavioral realism. For example, the time pressure relief during the remainder of the day, which may lead to additional activity participation, can be included as an element in the route choice between a tolled fast and a non-tolled slow route.A question now is how the input to such an activity-chain-based traffic assignment model may be obtained? Trip diaries provide the necessary data -i.e. a sequence of departure times, mode choice decisions, and activity locations -directly. A disadvantage of using trip diaries is, however, that all information that is taken from the diaries is by definition not sensitive to policy measures. For example, if one wants to investigate departure time reactions to a policy measure, one cannot take the departure times from the trip diary. Instead, a model component needs to be built that endogenizes departure times in a meaningful way. Also, trip diaries ...